{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AMAZON STOCK PRICE PREDICTON\n",
    "\n",
    "### Time Series Analysis\n",
    "\n",
    "### Abstract\n",
    "\n",
    "Any data that collectively represents how a system/process/behaviour changes over time is known as Time Series data. Time series forecasting uses information regarding historical values and associated patterns to predict future activity. In this project, we are going to predict tomorrow's closing price of Amazon Stock (AMZN) by analysing the stock data for over a time period(3 years) until today. Predicting the performance of stock market is one of the most difficult tasks as the share prices are highly volatile for various reasons. Thus, forecasting prices with high accuracy rates is is difficult. Stock market are key to technical analysis where the approach to investing is based on cycles. If we want positive return every time whenever we do investment, we can take help of a machine learning model that can help us to look deeper into these hidden cycles in stock market. ARIMA and FBProphet are common statistical models used to predict stock prices. Also deep learning technique of RNN, specifically LSTM runs on Tensorflow background and is known to have better performance.  We shall be implementing these models and comparing the performance of each model and choose the best one. In order to handle big data and be time efficient, we shall be running this code on Spark clusters.  \n",
    "* Code can be implemented on Databricks\n",
    "* Code can also be uploaded and implemented on VM Instance of Google Cloud platform\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Languages & Tools\n",
    "* Python\n",
    "\n",
    "### Environment \n",
    "* Spark\n",
    "\n",
    "### Cloud Platforms\n",
    "* Data Bricks\n",
    "* Google Cloud Platform\n",
    "* Google Dataprocs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing  Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from datetime import date\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import fix_yahoo_finance as yf \n",
    "import pyspark as spark\n",
    "from io import StringIO\n",
    "import time, json\n",
    "from datetime import date, timedelta\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "#from statsmodels.tsa.stattools import adfuller, acf, pacf\n",
    "from sklearn.metrics import *\n",
    "from matplotlib import style\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "#from matplotlib.pylab import rcParams\n",
    "import warnings\n",
    "from prettytable import PrettyTable\n",
    "#rcParams['figure.figsize'] = 16,8\n",
    "from pyspark import SparkConf\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import collect_list, struct\n",
    "from pyspark.sql.types import FloatType, StructField, StructType, StringType, TimestampType\n",
    "from fbprophet import Prophet\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.models import Sequential\n",
    "from sklearn.model_selection import  train_test_split\n",
    "import time #helper libraries\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from numpy import newaxis\n",
    "#from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Data\n",
    "\n",
    "### Starting Spark session: \n",
    "\n",
    "Data is pulled from Yahoo Finance from today's date back upto 3 years so as to take into consideration the most recent trend for better predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a SparkSession using the SparkSession.builder method (that gives you access to Builder API that you use to configure the session).\n",
    "\n",
    "spark = SparkSession.builder.getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.now() # gets today's date\n",
    "start = start.replace(year=start.year-3) # gets the date before 3 years\n",
    "end = datetime.now() # gets today's date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loading data from  `fix_yahoo_finance` api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_csv():\n",
    "    amzn_data = yf.download('AMZN',start,end) # getting amazon stock from yahoo finance\n",
    "    amzn_data.to_csv('AMZN.csv', index=True, header=True)\n",
    "    amzn_data.Close.plot() # Plotting the close value of stocks\n",
    "    plt.show()\n",
    "    return;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 downloaded\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = (spark.read\n",
    "          .option(\"header\", \"true\")\n",
    "          .option(\"inferSchema\", value=True)\n",
    "          .csv(\"AMZN.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[summary: string, Open: string, High: string, Low: string, Close: string, Adj Close: string, Volume: string]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(Date=datetime.datetime(2016, 4, 26, 0, 0), Open=626.169983, High=626.75, Low=614.880005, Close=616.880005, Adj Close=616.880005, Volume=2521400),\n",
       " Row(Date=datetime.datetime(2016, 4, 27, 0, 0), Open=611.799988, High=615.950012, Low=601.280029, Close=606.570007, Adj Close=606.570007, Volume=4068800),\n",
       " Row(Date=datetime.datetime(2016, 4, 28, 0, 0), Open=615.539978, High=626.799988, Low=599.200012, Close=602.0, Adj Close=602.0, Volume=7872600),\n",
       " Row(Date=datetime.datetime(2016, 4, 29, 0, 0), Open=666.0, High=669.97998, Low=654.0, Close=659.590027, Adj Close=659.590027, Volume=10310700),\n",
       " Row(Date=datetime.datetime(2016, 5, 2, 0, 0), Open=663.919983, High=685.5, Low=662.030029, Close=683.849976, Adj Close=683.849976, Volume=6578500)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Description\n",
    "\n",
    "The data pulled from Yahoo Finance is stored in a .csv file. The AMZN stock data has the following features:\n",
    "\n",
    "1. Date - in format: yyyy-mm-dd\n",
    "2. High - Highest price reached in the day\n",
    "3. Low\t - Lowest price reached in the day\n",
    "4. Open - price of the stock at market open \n",
    "5. Close - price of the stock at market close\n",
    "6. Volume - Number of shares traded\n",
    "7. Adj Close - stock's closing price on any given day of trading that has been amended to include any distributions and corporate actions that occurred at any time before the next day's open\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Running basic spark commands for exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only Date and Close columns which we shall be using in our model\n",
    "df = df.select(\n",
    "        df['Date'],\n",
    "        df['Close'].cast(FloatType())\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(Date=datetime.datetime(2016, 4, 26, 0, 0), Close=616.8800048828125),\n",
       " Row(Date=datetime.datetime(2016, 4, 27, 0, 0), Close=606.5700073242188),\n",
       " Row(Date=datetime.datetime(2016, 4, 28, 0, 0), Close=602.0),\n",
       " Row(Date=datetime.datetime(2016, 4, 29, 0, 0), Close=659.5900268554688),\n",
       " Row(Date=datetime.datetime(2016, 5, 2, 0, 0), Close=683.8499755859375)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+------+\n",
      "|               Date| Close|\n",
      "+-------------------+------+\n",
      "|2016-04-26 00:00:00|616.88|\n",
      "|2016-04-27 00:00:00|606.57|\n",
      "|2016-04-28 00:00:00| 602.0|\n",
      "|2016-04-29 00:00:00|659.59|\n",
      "|2016-05-02 00:00:00|683.85|\n",
      "|2016-05-03 00:00:00|671.32|\n",
      "|2016-05-04 00:00:00| 670.9|\n",
      "|2016-05-05 00:00:00|659.09|\n",
      "|2016-05-06 00:00:00|673.95|\n",
      "|2016-05-09 00:00:00|679.75|\n",
      "|2016-05-10 00:00:00|703.07|\n",
      "|2016-05-11 00:00:00|713.23|\n",
      "|2016-05-12 00:00:00|717.93|\n",
      "|2016-05-13 00:00:00|709.92|\n",
      "|2016-05-16 00:00:00|710.66|\n",
      "|2016-05-17 00:00:00|695.27|\n",
      "|2016-05-18 00:00:00|697.45|\n",
      "|2016-05-19 00:00:00|698.52|\n",
      "|2016-05-20 00:00:00| 702.8|\n",
      "|2016-05-23 00:00:00|696.75|\n",
      "+-------------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select(df.Date, df.Close).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting into pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index =df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-04-26</th>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>616.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-27</th>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>606.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-28</th>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>602.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-29</th>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>659.590027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-02</th>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>683.849976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date       Close\n",
       "Date                             \n",
       "2016-04-26 2016-04-26  616.880005\n",
       "2016-04-27 2016-04-27  606.570007\n",
       "2016-04-28 2016-04-28  602.000000\n",
       "2016-04-29 2016-04-29  659.590027\n",
       "2016-05-02 2016-05-02  683.849976"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "indexed_df = df.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-04-26    616.880005\n",
       "2016-04-27    606.570007\n",
       "2016-04-28    602.000000\n",
       "2016-04-29    659.590027\n",
       "2016-05-02    683.849976\n",
       "Name: Close, dtype: float32"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = indexed_df['Close']\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-04-22    1887.310059\n",
       "2019-04-23    1923.770020\n",
       "2019-04-24    1901.750000\n",
       "2019-04-25    1902.250000\n",
       "2019-04-26    1950.630005\n",
       "Name: Close, dtype: float32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize time series of AMZN stock for Close price trends over time 3 year's time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Close Price')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(ts)\n",
    "plt.title('AMZN 3 years trend')\n",
    "plt.xlabel('Year',fontsize=20)\n",
    "plt.ylabel('Close Price',fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check and Understanding Stationarity of the Time series\n",
    "\n",
    "* tionarity means that the statistical properties of a process generating a time series do not change over time. It does not mean that the series does not change over time, just that the way it changes does not itself change over time. The algebraic equivalent is thus a linear function, perhaps, and not a constant one; the value of a linear function changes as 𝒙 grows, but the way it changes remains constant.\n",
    "\n",
    "* Stionary Time Series data does not have any upward or downward trend or seasonal effects. Mean or variance are consistent over time\n",
    "\n",
    "* Non-Stationary Time Series data show trends, seasonal effects, and other structures depend on time. Forecasting performance is dependent on the time of observation. Mean and variance change over time and a drift in the model is captured.\n",
    "\n",
    "* We are using a statistical method called Dickey-Fuller test to check if our time series is stationary or not.\n",
    "\n",
    "* In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter.\n",
    "\n",
    "### Implementing Dickey-fuller's for Stationary test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    \n",
    "    rolmean = timeseries.rolling(window=52,center=False).mean() \n",
    "    rolstd = timeseries.rolling(window=52,center=False).std()\n",
    "\n",
    "    \n",
    "    plt.figure(figsize=(16,8))\n",
    "    orig = plt.plot(timeseries, color='green',label='AMZN Data')\n",
    "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.xlabel('Time(Days)',fontsize=15)\n",
    "    plt.ylabel('Price($)',fontsize=15)\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for checkinh Dickey-fuller's test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -0.232047\n",
      "p-value                          0.934592\n",
      "#Lags Used                       8.000000\n",
      "Number of Observations Used    747.000000\n",
      "Critical Value (1%)             -3.439134\n",
      "Critical Value (5%)             -2.865417\n",
      "Critical Value (10%)            -2.568834\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Because the test statistic is more than the 5% critical value and the p-value is larger than 0.05, the moving average is not constant over time and the null hypothesis of the Dickey-Fuller test cannot be rejected. This shows that the weekly time series is not stationary. Transform this time series into a stationary time series before fitting to the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_log = np.log(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B1WrFycnpoudMnDixTOWEEEIIIYQQojxV+OC1PMyZM4fHH3+cKVOmFO/r3Lkz69atIyQkpHjfyy+/TOfOnfH19S11/oQJE3BycuL555+/7HVee+21Mve8pqSk8OyzzzJy5EiUUqSnp1OlShVMJhPTp0/HYrEA4O7uTmZmZvF5lyonhBBCCCGEELcTSdh0DSIjI3nwwQdL7RswYMAFWXwbNmxIRETEBee//vrr7Nu3r9RyOV27dr2gXMOGDUsNRT5fTk5O8VI53bt3p2fPnowbNw6A5557junTp9OmTRsOHjyIq6srAE2aNMHOzo6wsDAmTpx4yXJCCCGEEEIIcTtRRXMkb1ctWrTQ5ycQ2rdvH/Xr1y+nFonzyeshhBBCCCGEuBZKqW1a6xZlKSs9r0IIIYQQQlyB1poCS0F5N0OIu5rMeRVCCCGEEOI8Wmtiz8Sy8NBC1sWtY13cOtLz0tn05CYa+je8pjpzCnJYErOEhn4N0Wi8nLzwd/W/wS0X4s4lwasQQgghhBAlaK2576f7WBKzBIAQjxA6VuvI8iPLGfbLMDY9uQlHO0dSs1MZtXgUcelxNAtsxvvd3sfV4eL5Q3Yn7+aFRS+wKnZV8T47kx2/DvqV++te3dKIQpSV1hqlVHk344aR4FUIIYQQQggbrTVLYpawJGYJgxsN5p2u71DbpzYAvx/4nX4z+hH6RShtQ9py8uxJ1sWto02VNny+5XOWHVnGzIEz2ZG0g5SsFOzN9oxoMYLNCZvp8UMPsguyGdJoCL1q98KszHy88WP6z+jPlD5TeLLZk+V85+JOorVGY3wJM7D+QJ5q/lR5N+mGkOBVCCGEEELckQ6mHSS7IJsA1wAOph3ktRWvMbTxUJ5r+VxxmUJrId9s/4bU7FQOnTrEqthVxKXH4e/qz9R+U3Gxdyku2ze0L1P7TWXuvrlsjt9M7JlYxrQfw4fdP+TPI38ybN4wmk5pikUbSw86FMJnc/+JU2Yuna1uTA7/gBoxJlj3F5w+zSNpldl76Cim/z5FnuO/cbR3ArMZTCbjZ8nfi366u4O/PwQElP5Z9HtgINjJR/y72ab4TfSL7EdVz6psS9zG0EZDy7tJN4xkGxbXTV4PIYQQQtwqh08dxsnOiSoeVS5ZptBayLiV43h/3fsXPW5vsqeqZ1V8nH1Iy0njyOkjAPi5+NG5eme6VOtC/3r9L3sNgHxLPvbZeajt2yEqiqwjB9gWvQjvjHxqnQanxBRM1ot81nZ1BR8f8PYm182ZVenRpJny8HepRKCLP87KkVpeNVAWC1itUPJnRgYkJ0NSEmRnX1i3iwu0bw9Nm0Lt2hAcDEFBxs9KleAOGkIqIDMvk98O/Ea+JZ9j6cdYG7eWjcc3klOYA0DX6l358/E/ManbN0/v1WQblq9lrpHZbKZx48YUFhZSo0YNfvjhB7y8vC5ZPjY2lj59+rB7925WrVrFhAkTWLBgAb/99ht79+5l7Nix192m4cOHM2vWLJKSknB3dwdg1KhRTJo0iZSUFCpVqnTd1xBCCCGEuJkKLAUkZCbw6aZPWRe3jlfav8LDDR8G4FDaIep+URdPR0/SxqRhNpmLz5u1ZxbpuekcSDvAvH3zOHrmKI+HPU7LoJYoFNW8qhHqG8r/tv8Pi9XCyayTnMo5hb+rPx90+4AH6j2Ag9mhdGMKC+HoUSNYTE2FlBQ4fBh27oS0NBxOnYIjR4zAEnB1d6eTvz/4VYHGNaBOHahSBby9wcvL6BWtWhU8PIov4QQ0z0rhy61f8tH+X9iRtAOAIy/OpYZ3jeJyG49vJDU7lb6hfc+1LyvLCGJLbrt3w9q18OmnkJ9f+n4cHY32hIRAtWrQrRv07g2+vjfglRNlpbUm35KPo53jZcslZCSw/vh6ft71M3tT9pKSnUIll0oUWArwcvLCoi0kZiaSlpMGgEIRFhjG35r9jRdavUCAWwBuDm63deB6tSR4vUbOzs5ER0cDEBERweTJk3nttdeuup5+/frRr1+/G9au2rVrM3/+fIYNG4bVamXlypUEBwffsPqFEEIIIW6GpLNJ/N/8/2NJzBKs2ggGvZ28eWTOI3gv8KaObx0SMhIASM9Lx+4dOx4Pe5zXOr5GXHocg+YMAoxe1W41u/Fxz495oN4DFySrGd9j/Lk/rFZIT4fYWJg5xwhUU1ONLSYGoqMhJ6d0Q+3toUEDIxCtUQOGDoXWraFlS/Dzu6Z793P1Y1yXcYzrMo41x9bQeVpnNsZvJN+ST03vmqyMXcm9P94LwOEXDlPLpxZHTh/B19kXz5o1oWbNCystLITEREhIOLcdPw7x8cbPP/6A6dON4citWkFoqBFYd+sGHTsa+8UNl5yVzH0/3UdeYR7Rz0ZjZ7LDYrVgNpmxaitLY5by2ebPWHl0JXmWPACqelY1voRRirj0OEJ9QzmTewZ7sz3hgeE81uQxanrXpJJLJbycLt2ZdieQ4PUGaNu2LTt37gSMb1LGjBnDokWLUErx+uuvM2jQoEueO23aNKKiovjiiy8YPnw4Hh4eREVFcfLkScaPH8/AgQOxWq2MHDmS1atXU6NGDaxWK0888QQDBw68oL4hQ4Ywc+ZMhg0bxqpVq2jfvj2LFi0qPv7jjz8yadIk8vPzad26NV9++SVms5kRI0awdetWcnJyGDhwIG+99RYA1atXJyIigt9//52CggJmz55NvXr1bvATFEIIIURFV2ApQKMv7L0sA601T/3+FCtjV/KPtv+grm9dwgPDqe1Tm083fUpSVhIH0w4S5B7E7IdnE7k7kuSsZGbvmc33O74HINQ3lKn9ptI4oDEejh5FFRs9pfHxcOqUEZD+9ZfRO3niBJw+XdxrWszNzQhCq1SBZ5+FsDCoXNkYclupkhG0Olz9PZZVu5B2uDm48c6ad9ifuh8/Fz/cHNxQKDSavpF9MSkTe1L2EOAaQCP/RuxO3s3Jf5wsXZGdndHDGhICGPN/z+afpVnlZsZxqxW2bYPff4eVK2HFCiPAfecdY0hzeLgRnAcFGb936WLsBz7Z+AkFlgJe6fDKTXsOd6ICSwHD5g1je+J2APpG9iUrP4stCVuoV6kep3JOcTzjOIFugTzd/Glqedeisntl+of2v2Iv7SXl5EBurtH7fweo+MHr6NHGt2I3Uni4MdSiDCwWC8uXL+fJJ40McfPmzSM6OpodO3aQmppKy5Yt6dSpU5kvnZiYyLp169i/fz/9+vVj4MCBzJs3j9jYWHbt2kVycjL169fniSeeuOj5derUYf78+Zw+fZrIyEiGDRtWHLzu27ePmTNnsn79euzt7Xnuuef46aefePzxx3nvvffw8fHBYrHQrVs3du7cSZMmTQCoVKkS27dv58svv2TChAl88803Zb4fIYQQQtx5rNrK5vjN+Ln6kZadxs6knby79l3sTfZEDoikZXDLi55XctkOrTWbEzbz7V/fsjRmKcfSjzG++3j+2f6fpc4Z12XcBfW0DWkLGL21U/+aSv7ZdF5y7Y7n0n0Qt8QITOPiICrKCFBLqloVmjSBDh2M4bI+PkagWr8+1KoFzs434AldOzuTHe1C2rE0ZikAnap14veDv/PJvZ+QlZ/FxE0TaeDXgI96fMSry19l+dHlAGTkZZwL2s+Tb8mn2ZRmZBVk8U3fb4zMxiaT0VvcssRrdfasEcwuX24MjV640BiKXJQjp1o1TrorwrJiSfW0x9o7HVNIVWjY0Aj4g4ONhFK3mfTcdFwdXLEzlU/ok1OQw6w9s/hs82f8dfIvvrr/KyJ3R7IneQ/Vvapzf9372ZO8h+ZBzfmg2wc83PDhq/sSqLAQDh0yhrdnZUFmphEfrVkDW7fCmDHw7rs37wZvoYofvJaTnJwcwsPDiY2NpXnz5vTo0QOAdevWMWTIEMxmMwEBAXTu3JmtW7cWB4JX8sADD2AymWjQoAFJSUnFdT788MOYTCYCAwPp2rXrZet46KGHmDFjBps3b2bKlCnF+5cvX862bdtoafufVE5ODv7+xsLYs2bN4uuvv6awsJDExET27t1b3OaHHnoIgObNmzNv3ryreEpCCCGEqMj+PPIn205sIywwjNo+tTmdc5oDaQf4Lvo7VhxdUapsiEcISVlJtP6mNWM7jOXNzm+yK2kXO5J2sP74eg6fOsyOkztoF9KO5pWb89vB39idvBsXexfurXUvb3d9m2FNhl3YiMJCIxBNTja2Y8dg1y7Yv5+AlBReTU2FtDTQtuHAShlZd4ODYcAAY0hvrVrngtQKML/z3a7vsjRmKUMbD+Wnh37CYrVgUiaUUrzW6dw0tRm7Z7AtcRsAe5L3FAf159uZtJOsgiwAXvnzFTpX71y8/E8pbm4wZIixFcnPNwKgtWuJW7uAfQfW460dqHskHz78EEompCoKiHv0ML4cqFHD6Pm9CV8IJGQksPDQQrydvBnYYOAl1zI9lXMK3/G+vNz2ZSb0nHBD26C1ZlfyLlYeXUnjgMbcU+OeUsfTstOYt28er698neSsZEJ9Q5kxYAaDGg3imRbPlOUCxns7Pv7c0O+iYd8HDxpfzJw5YwxzLygofa6dHbRoYXT09e59A++6fFX84LWMPaQ3WtGc1/T0dPr06cPkyZN58cUXud7szY6O54YEFNV1tXUOHjyYZs2aERERganEfAWtNREREXzwwQelyh89epQJEyawdetWvL29GT58OLm5uRe0yWw2U1hYeNX3JIQQQoiKJTMvk5l7ZvLMgmeK55+WFOQexCvtX6GOTx0C3QKp6lmVRv6NyMzP5O9L/s4H6z7g621fFyeSAWgf0p6BDQYSuTuS5UeX0ySgCV/3+ZpBjQYZPYbJybB4iTHv9MwZ40P69u2wY4cx7LEkHx9j3mnjxsZQ3oAAoze1SROjZ9Xe/mY/opuqZXBLdo3YRVXPqgClElOVVLKndVfyruLgNTEzkclbJ1PLuxbDmgxjc/xmAJYMW8LQuUPp/n131v7fWkI8Q67cGAcH1lSxMMzxS463Ok67Ae34pu83VP2yASarptoZWNbuv9QqdEfv20fagtn4fPA+JovtfWNnBz17GgFx//5X3TN7IPUAe1P20tC/IXV96wLwz6X/5JNNnxS/N3vU7MHUflMJ8Qzhj0N/8MehPxjSaAjtq7bns02fATBp8yQm9JyAxWphS8IWWgW3uuRzLYu1x9by9IKn2Z+6v3jfl72/ZETLESRmJvLGyjf4Zf8vnMo5RbB7MCseX0GX6l2MDNJ//QX79hmBaUaGsZ0+bbznk5ON9396uvHz/M/eJpPxfg8NNXq8vbyML2QaNjS+sHFzM7JZ16hhZJ6+w1T84LWceXp6MmnSJPr378+IESPo1KkTU6ZMISIiglOnTrFmzRo++uijUsHg1erQoQPTp08nIiKClJQUVq1axdChl16vqWrVqrz33nt079691P5u3brRv39/XnrpJfz9/Tl16hSZmZlkZGTg6uqKp6cnSUlJLFq0iC5dulxze4UQQghR8VisFlbGrmRa9DTm7ptLbmEuYQFhLBy6kH2p+0jISMDXxZcg9yDCA8MvmsHUw9GDb/p9w7217mXEwhEABLsEsqvHL3hn5MOpU0wJaIP5TDqmfSdg+Z+QEmnMSz1+vHRlnp7GVK4RI6BRI2Ouqb+/8QE9MPCOX/KlkX+jK5Z5reNrrD62Gqu2sjVhK083fxqACRsm8MmmTwD4YN0HeDh6EOgWSI+aPVj62FK6Tu9K52md+ajHR/Sv1/+Kw2ln7p7J8Yzj/L3N33mr61u42rsyuNFgvJ28+Xrb1zxX8AvB7sFEh0Tz14MH8bgP/mz8ES0tAcbw41mz4LHHjEC2enWjJ7zkVru2kXTqvB7aJYeXcP/P92PRRs/zY00eo0fNHny88WMGNBjAO13fYfmR5bzy5ys0/LIhTQKasP74ekzKxOStk2kf0p6oE8aSm4XWQr7c+iUrY1cyZ+8cetXuxS+DfsHJzolCayGvLn+V2DOxvNHpDRoHNOZ0zmm2JW7D0exIgbWADcc3kJmXSUZeBsfSj7Ho8CJqetfkv/f/l561evL8H8/z8tKXSc9L5+ONH0N6Bs9Zm/JwRgihf+XiOHMsnDxpbBfLAu3pee69HRpqBKWensbfwcHGqIGi43eQ6kaVAAAgAElEQVTxOr53753fQE2bNiUsLIwZM2YwbNgwNm7cSFhYGEopxo8fT2BgILGxsddc/4ABA1i+fDmNGjWibt26tG7dGk9Pz8ue88wzFw5FaNCgAe+++y49e/bEarVib2/P5MmTadOmDU2bNqVhw4bUrFmT9u3bX3NbhRBCiDtJZl4ma46toZJLJap5VSPQLbC8m3SBQmshWxK2sOPkDnYn7ybmdAyhvqG4Objh4ejBU82fwsfZp9Q5h9IOMX3HdLYlbiM5K5m8wjxSs1NJykrC1d6VJ8KfYHCjwbSv2h6TMhHsUYaVC6xWY57d2rU8HBfHwBPdIfYY7NyJGnNuOGtxn6iLizGktFIlY4hp8+bGMMfQUCO5jOM1Jqi5i3Sr2Q3Lmxb+b/7/MW3HNF5o/QKN/Bsxe+9s+tTtw7PNn2Xs8rFEn4xm4r0TUUrRrHIzFj26iIdmPsTA2Ubyz3+2+2fpLMznWRO3hp61evLxvR8X74scEAlAQmYCvx34jUC3QEJ9Q3n/nvd5Z807fO68k+8fNJJp8Z//wIYNsHix8UXF4cOwebPRs1iSvz/UqIFu0YKNxLPg2O+M8Q1iWPsRLDsVxbxls5mcO52uVsV/a3Sn0pxV1LMqBjmM4addP5G4dSdjq/eha+i9bEjczLSFP1JFQdda97Ds6ApW734eJ60YbfLAce1ilkc2oUtgO2JPxVDn2DrqWSH6/bnsd3IjyZJBjj3k2EG2PeTagYMy4292pK6DBwPt69Kn9n34L06F/GnMOuzKtk15VPn4X7yYbcIlzwpsNrY6dYygvV49I/lXeLiRCMzPz+iJlvd6manrHeZ6s7Vo0UJHRUWV2rdv3z7q169fTi0qH2fPnsXNzY20tDRatWrF+vXrCQy8Pf4BvRtfDyGEEHe29Nx0HvvlMRYfXkyB9dxcss7VOvNcy+foXac37aa2I6sgizkPz6Fp5aY39PolExsVWRqzlPVx66nsXpnlR5djsVrwdPLk1/2/cibXCAIUikb+jYg5HUNeYR4WbQHAx9mHjlU7kpyVzIG0A5zKOQVA/Ur1qeldE0c7R5ztnOlbty/31bnvkol/imVnw/r1RgBy5Iix1MyuXcbcOwAnJ6OXKCTEGMrbrNm59U59fIyfHh53fO/prZKWnUaDLxsQ4hHCxHsn0mlaJ3548AeGNRmGxWohtzAXVwfXUufkFebRaVontiRsAc4NeS2pwFJAxK8RRO6O5N2u75aab1uyTE5hTqn3zFO/PcWPu35kxeMrLjkPFzAyQB8+bGSBjokx5jMfPEjulg045d78qWpn7SHHHiwKlJ0dldz8OZ2fAZZCXAsVDvlWzLl5V65IKQgKIic4gLRKrlSuHY65chA0bQpt25Za11dcSCm1TWvdokxlJXitGLp06cKZM2fIz89nzJgxDB8+vLybVOxufD2EEELcmbLys5j611Sm/jWVvSl7Gd16NL1q9yIuPY7jGceZFj2No2eO4mLvQnZBNgAtglqw6clNvLbiNZbELGHDExtwtr+2BDUWq4VH5jxCanYqS4ctLV4eo8BSQPAnwaRkp5Qq7+7gzoP1H6RPnT7U8a2Dq70rdXzrFB//7cBvfLLxE6p6VmVT/CaC3IMI9Q3FrEy83OJFajkHGfNJi5bTyMm58PesLCNhUmKiMTfv+HHYtOnc0MfKlY35dXXrwj33GFtQkASmt9icvXN4ePbDgLHWbeqY1Ct+CVFoNQLEB2Y8wJKYJfz52J90rt4ZML5AGbFwBFO2TeG+2vfx3/v/SzWvamVqS9LZJDp814HU7FReaPUCJ8+eZEqfKZdMqlTyvPkH5vPM788wqvHf+KTdW5jOZhlZkNPSjPelg4Mxp9nb2+ipLTmEVmuwWIz3psVCYUEeCWeOU829ijEywM7O2Bwc0L6+zI//k8TMRFwdXOlVuxf+rv4XNkpr47p5ecZ808tt4ppI8CpuKXk9hBBCVBRaa7YlbmPm7pmsP76e1sGt6V2nN4Fugfy862d+2PkDCZkJNPZvzJud32Rgg9Jrqlu1lSWHl/DVtq/wc/GjY9WODJ8/nLZV2rIxfiNAcTbRa/HykpeL5yp6OHpQ17cuI1uOxMPRg4dmPcSvA+fSyrUuTpk5uJ/Nx3wyGZWcbASV6enGVpQApuSWnW18oC/acnMvXN/0Snx9jTl4fn7QqZMRpLZvf1sujXK3GjJ3CDN2z+CBeg/wy6Bfynxeem46baa2ISUrhX91+BfH0o/hYu/Cf9b/h7Htx/JB9w+uXMl5jp05RofvOhCfEQ/Alr9tueQSSgA7Tu6gT2Sf4vL7n99PaKXQq76uqHgkeBW3lLweQgghbndWbeX7Hd/z484fWX50OfYme+r71Wdn0s7iMmZlpnvN7rza8VU6VSvbGu35lnxqfFaDE5knGNd5HN/+9S2NAxqzcOjCizTCamQUzc01Asi8vHPBZF4eB07s5u+/PstjPl1omu9LUvwBTqXGQVYWIdqDoPh0KmdcJuA0mYzg0sPjws3FxZhXV9Rr5eRkJMcp+lny9/P3ubgYQ4DLef1TcWUWq4W1cWupV6neVc/PPpR2iDZT2xQPKQfoH9qfeYPmXTQ5V1kcSD3A++ve5/sd3zM8fDjf9f+u+JjWGo1m7t65TP1rKsuOLKOyW2USMhMAsL5pvWJPrbgzXE3wKgmbhBBCCFEhJGclk5CRUGp+qVVbL/hgrbVmWvQ0Vh9bTYugFmxJ2MKaY2s4ln4MH2cfPurxEU82fRJvZ29yCnL4ZOMneDl58XDDhy8+bPAyHMwOzOj3AzkH99AzpzZhMeuIm72YM993wdNijyoagnv6NNb445jyCy5ZVyhghLyrwM6OUC8vch1diMnPINPhNImtGlG57YNGD2jRvNHKlY1lM7y8jOUx5MP+Xc1sMtOlepdrOreObx2inopi5p6ZPNH0CXac3EGHqh2uOXAFCK0UyvQHplPJuRITN03k+ZbP0yLIiFFGLx7NpC2TAGON4Fc7vMrzrZ4nPTedAmuBBK7ioqTnVVw3eT2EEELcbCuPruSe7+8BYGz7sdxX5z4a+jUkfEo4zzR/htc7vV5c9rcDv9F/Rv/iv/1c/OhYrSMD6w9kcKPB1/+hWGujB3XVKoiMhEWLjHmhNln2cMQb3HwDqV65AcrJiRS7fL5N+5PKdZvxeNtnOZwZx/92T+dQ1nHc3X1wdfXmYGYsTaq14pMnZhpzRs1mrNrK4DmDic+IZ8bAGcXrfgpRkWTkZeD3kR+jWo9ifI/xFFgK8B3vS2Z+Jl/c9wUjWo64riBZVGwybFjcUvJ6CCGEuBky8jJ4bflrLI5ZzOFThy843jKoJVtPbAVgz3N7aODXgKz8LMKnhGNnsmPDExs4lXOKmt41rz9gPXsWZs6E776DHTuMv8FIGPPQQ0ZG0Vq1sNaozqMbXmb50RWkZKcQERbBpPsmce+P97IpfhMAvWr3YnvidpKzklEoanrXpJZPLRr5NWJ0m9GEeIZcX1uFuA11/K4jBZYCNv1tE8uPLKf7D92Z98g8Hqz/YHk3TZQzGTZ8C5jNZho3bkxhYSE1atTghx9+wMvL65LlY2Nj6dOnD7t372bVqlVMmDCBBQsW8Ntvv7F3717Gjh173W3atGkTo0aNIi8vj7y8PAYNGsS///1vVq1ahYODA+3atbvoeW5ubpwt+kdYCCGEKGdaazLzM3lo5kOsPraa+2rfx3MtniMiPALf8b4AjGo9ikmbJxHkHsSJzBPM3z+fKh5VGDp3KDGnYlgRsQJvZ2+8nb2vrRFnzxo9qnv2wIEDsHAhZGZCgwbwxBNQrZqxTmPnzqWynZqAyIEz0Frzzpp3GLdqHL8d+I30vHTe6PQGy44sM5Y18WvAgiELLpvARog7Sedqnflw3YfsTdnLL/t/wdnOmXtr31vezRIVjASv18jZ2Zno6GgAIiIimDx5Mq+9duHaV1fSr18/+vXrd0PaFBERwaxZswgLC8NisXDgwAEAVq1ahZub2yWDVyGEEOJ28vmWzxm1eBQA0x+YzuNhjxcfOzjyICZlopZPLZ5s+iQejh4MmDWACRsn8L/t/yMuPY7JvSdf/by/nBxjCZhDh2D2bJgzxxgKrBRUrQoPPghPPw3t2pVpXqlSijc7v4mnoydvrHyDnx76icGNBvN217evrl1C3CEGNxrMF1u+IOyrMBzNjvSs1RMXe5fybpaoYCR4vQHatm3Lzp1GtkKtNWPGjGHRokUopXj99dcZNOjS6fKnTZtGVFQUX3zxBcOHD8fDw4OoqChOnjzJ+PHjGThwIFarlZEjR7J69Wpq1KiB1WrliSeeYODA0un7k5OTqVy5MmD0DDdo0IDY2Fi++uorzGYzP/74I59//jlVqlRh6NChFBYW0qtXr5v3YIQQQoirtOH4BsatGoeD2YGv+3xdKnAFSq1h2jigMQAjW43kiflPYFIm/nz8z7IHrmlpMG8ezJhhzF8tWjrGwwOGDIHHHoPWrY0svddoVJtRvND6BZnPJ+56jfwbceiFQ7yx8g3+t/1/F/y3LURZVPjgdfTo0cU9oDdKeHg4n376aZnKWiwWli9fzpNPPgnAvHnziI6OZseOHaSmptKyZUs6dSpbun2AxMRE1q1bx/79++nXrx8DBw5k3rx5xMbGsmvXLpKTk6lfvz5PPPHEBee+9NJLhIaG0qVLF3r16kVERATVq1fn2Wefxc3NjX/84x+A0ds7YsQIHn/8cSZPnlzmtgkhhBA3wtTtU/nP+v+g0SgUtX1q06duH9bGrWXG7hkEuwez8cmN1KtUr0z1DQ8fTruQdng6ehLgFnDpgrt3G4HqihUQGwuJicb+OnVgzBho2NDoZW3Z8oYuCyOBqxAGP1c/vurzFRPvnYizvSy9JK5ehQ9ey0tOTg7h4eHExsbSvHlzevToAcC6desYMmQIZrOZgIAAOnfuzNatW2nSpEmZ6n3ggQcwmUw0aNCApKSk4joffvhhTCYTgYGBdO3a9aLnvvnmmzz66KMsXbqUn3/+mcjISFatWnVBufXr1zN37lwAHnvsMV555ZVreAJCCCHE1duTvIfn/niOBn4NqF+pPlZtZWXsShYdXoSj2ZHXO77O2A5jcXVwvap66/rWPfdHVhasXg2LFxvJlZKTISnJyBBsMhlDf++7D2rWhN69ITxclpgR4haSwFVcqwofvJa1h/RGK5rzmp6eTp8+fZg8eTIvvvgi15u92bHE0KSiuq6mzlq1ajFixAieeuop/Pz8SEtLu2g5WTtLCCHErZJvyWf5keXM2TuHOfvm4O3kzeJHFxf3khZYCkjJTsHT0fPqglatISoKliyB6GiIjzcC1NhYyM83ek+bN4dGjeCee4xkSwMHGuuiCiGEqHBkHMt18vT0ZNKkSUyYMIGCggI6derEzJkzsVgspKSksGbNGlq1anVd1+jQoQNz587FarWSlJR00d5UgIULFxYHuocOHcJsNuPl5YW7uzuZmZnF5dq3b8+MGTMA+Omnn66rbUIIIcSlaK15e/XbBEwIoPfPvZmzbw5tq7Tl9yG/lxrea2+2J8g96MqB65Ej8Mkn0L071KoFrq7QqhW88Qbs3GnMVQ0Ph1GjYOlSOHUK1q41EjBNngzPPy+BqxBCVGAVvuf1dtC0aVPCwsKYMWMGw4YNY+PGjYSFhaGUYvz48QQGBhIbG3vN9Q8YMIDly5fTqFEj6tatS+vWrfH09Lyg3A8//MBLL72Ei4sLdnZ2/PTTT5jNZvr27cvAgQOZP38+n3/+OZ999hlDhw7ls88+Y8CAAddx50IIISqKnIIcdibtJLsgm47VOmJnuvqPANkF2TjbOZdp9M6R00d4dsGzLDuyjP6h/flbs7/Ro2YPHO3KmPzo+HFYtswIPuPi4NgxiIkxjjVuDG3aQGAgNGkCffqAr+9V348QQoiKRV3vMNebrUWLFjoqKqrUvn379lG/fv1yalH5OHv2LG5ubqSlpdGqVSvWr19PYGBgeTcLuDtfDyGEqChSslIYOm8oK4+uxKItAPSs1ZPhYcMZ1GjQRZMJFVgKOJ17mkoulfh1/68sOLiADcc3cCDtAJXdKvNcy+foWasnVm0l9kwsB1IPsCJ2BbW8a9E+pD0LDy1kacxSHMwOjG4zmtc7vX7hdXJzISPD2I4cgU2bYNcuSEkxhv8WBap+flC7NgQHG3NV+/c35qoKIYS4IyiltmmtW5Sp7K0MXpVSocDMErtqAm9qrS85cVWCV0OXLl04c+YM+fn5jBkzhuHDh5d3k4rdja+HEEJUBEdPH6Xnjz1JyEhgdJvRtAxqyYw9M5i1ZxYAfer2YVKvSVT3qs7svbP5Lvo7DqYd5NiZY1i0BXuTPQXWAnycfWgf0p6mgU1ZfWw1q4+tvuBaTQKasDPJWDauikcV7q9zPy+3ffnc0jYWizEvdd48Y9u/v3QFShlZfwMDwd8f2raFHj2M+aqSp0EIIe5YVxO83tJhw1rrA0A4gFLKDCQAv9zKNlRUl5rnKoQQ4u6iteaPQ3+QeDaRZpWbkV2QzdHTR9lwfAMF1gJCPEJoVrkZdiY7RiwcQUZeBssfX07bkLYAdK7eGW8nbwqthUTujqT+5PqEeIZw+NRhannXolVwK4Y0GoK3kzdx6XG0rtKaQQ0HYTaZi9uQnJXM8iPL8XD0oLpXdap5VcPNwY0dCduxz8ymvkMQ6vRp+PEPY+7p7t1w4gQUFoLZDJ07w9Ch4ONjzFMNCjKWp/HwKK/HKoQQogIozzmv3YAYrfWxcmyDEEIIcVvRWhOfEc+WhC2kZKfQPqQ9zvbOTN4ymeikaHYm7eRUzqky1RXgGsCyx5bRPKh58T4fZx++6vMVAG91eYt/Lf8XUSeimNx7Ms+2ePbKa5JqjX9iBkN2aUiNgfTtxlDfbdsI27YN8vJKl69bFzp1MtZPrVfPWJpG5qcKIYS4BuUZvA4GIq/1ZK21LPdyG7jd50wLIURFM/bPsYzfMP6C/U52ToQFhDGw/kDaV21P6+DW7E7ejbujO34ufgR7BJOanYq3kzfxGfGk5aTRvHJz/Fz9LnmtYI9gvn/w+0s3Ji8PNm40tsREI4nSpk1w8mTpcm5uEBZmZPOtVs3oQfXwgGbNoHr1a3wSQgghRGnlErwqpRyAfsC/LnH8aeBpgKpVq15w3MnJibS0NHx9fSWALUdaa9LS0nBycirvpgghxB1jQ/wGGvo15Nv+3+Lh6MHaY2uZvXc2H3T7oFQPKkBopdBSf/u7+gNQ2b3y1V84IwPWrIG//jKC05gYI9NvdrZx3MvLmI/arRt07GgkTwoOBnd3sLe/pnsVQgghrka5ZBtWSvUHntda97xS2YslbCooKCA+Pp7c3Nyb1URRRk5OTlSpUgV7+eAihLhKWmsid0eyOX4z/2z/T6p4VCnvJt0Wqn9anY7VOvLDgz/cnAvk5sLBg0ZgeuiQsRbq4cOwZYuRVAmMuahVqhjDfbt3hy5d4CJLtAkhhBDX67ZN2FTCEK5jyLC9vT01atS4gc0RQghxM2UXZLMsZhlHTh/BbDKzKnYVy44s42z+WQDWxq0l6umoK8+3vMNZrBYSMhMI8Qi5vory8uDMGdi5E7ZtM4LTmBjjZ0ICFH1x7e5uBKrBwfDKK0ag2qYNODtf/80IIYQQN9gtD16VUi5AD+CZW31tIYS428Slx7Ho0CLiM+IpsBbg4ehBTkEOf2/7d7ydvS95nlVbSTqbxJncM1TxqIK7o/tVXXdT/CZ8nH0otBbyV+JfTNw0kW2J24qPB7sHM7TRUJpVboaTnRPD5w/nuYXP8Vmvz3C0c7zm+71dWawWYs/EcjzjOPEZ8aRmpzKg/gBCPEsHqUlZSRRaC8sWvObmGsFpQoKRyffoUYiKMob9ZmSULhsQALVqQdeuxpqptWsbS9FUry7L0AghhKgwbnnwqrXOBiTNoBBC3ECLDi3i082fkpadRoG1gEJrIZ6OnuxI2kF2QXZxj6ZVWwGwaAvvd3u/+HyrtjJ7z2w2J2xmf+p+DqYdJOZ0DAAmZaKxf2OGhw/H1d6ViPAIHMwOF7ThUNohvtz6JYtjFrM/tfQanu4O7szs9S3d7eqSdyyGwEIn1Ml02HsKffw4zaJrc+ynKawbMw1nsxP2JjsCXQOxWgvJLcjFpI3MuR4ObqA1Vm0lvyAPB7M9JmcXcHU1kga5uoKLCzg4XHyzt7/4fpMJlGJjwmb+s2E8Hap2pF/9/tSpFIqyHUOp4nLY2xsZcytVuqCX8kzuGb7f8T2nc06TZ8lDoZi+YzoJmQmlyr239j2+6fsNvev0xt5sTL2IS48DoKpniXwPmZlGkHrwIKSlGYHqpk1GoFpQcK6cgwOEh8Ojjxo9qV5exrqprVvLkF8hhBB3hHKZ83o1LjbnVQgh7kZaa+LS46jmVQ2AE5knWHF0BRuPb2TKtikEewTT2L8x9mZ7zMrMqZxTBLoF8nqn16nrW5cTmSdIz01n3Kpx/HnkT55v+TyF1kKm/jWVzPxMCq2FONk5EeobilLK6Bn0COHomaPMPzCf6JPR2FlgqE8n3mo8iuoOfmSfTmb93iXsPrye5Ni9+OQpapv9qJLnRHCmwj09B+fsAszZOahL5Snw9ITgYM6Qx9H0WEwmM1as5FsL0YAyKayAFU1Nn1rkFOaSnJ1MnrUAszJT3706vlYnOHsWa9ZZyMrCVFBorCl6Kzg5Gffg4UG+iyNbsw9xwj6PfDNYTIoCk8bfI5DafvVwc/XG1dkDi9XCH4f+4Ez2KVzsnWlQqQEF1gIOpB7AlJvHYI+2uJ/OhqSkCzP7OjpC8+bQoYMxxLd6dWOdVD8/I7gWQgghKpCrmfMqwasQQpSjQmsh0SejqepZtThTrMVqYW3cWnac3IHZZOajDR9h1VYczY7EnI6hkksl3BzciD0TC4BC8WiTR/nv/f/FzcHtitc8fOowIxaOYOXRlVi0BS9HTxzS0hng0pzP27yNOSPT6OE7fNjYUlOxnDnN2ZNxuJ/JxnSZfza0gwPKy8volQwOBn9/Y8kUd3fw9jbW+gwJMeZZenoa+9zOtfls/lmc7JzIt+QTlx6Hv6s/3k7eZORl0OSrJsSlx2FnsuP+OvfTs1ZPvov+jh0nd9C0clNqetck+mQ0B9MO8kzzZ3ih5Ug8TM4cT43h6bnDOXv2FPcEd+Dwyb0knUnAwQIOFnAsBJOGuj61aVelLQ/VfQAFbDy+gU3HN/LXiW3kFeRhVoq+te/n/tq9CXEOYN/B9WzdsYj0E0fwLbDHt9ABc0YmbrlWmtiH4KzsobAQa0EeZos2gunCwuLeUm0yUaALybcWUGAx9imlKLQz4V03DHPlIGO4b/XqRo9qgwZGgOrmJkN9hRBC3DEkeBVCiApgxdEVjFo8it3JuzErM91rdqeGVw1+O/gbJzJPFJcL9Q2lbUhb0rLT+P3g7wB0q9GNXrV70a1GNxoHNMbOdIVZIAUFEB8Pu3YZa3bu2kVhUiL5SSdwTstAFS2HUpKzszE3MiDACDS9vIweUj93VmftY2nSenIdzUR0eB5nnwBahve+qYl+Vh5dydfbv2Z89/HFc0VTs1N5e/Xb7E/dz5HTR8jMz6SKRxV2Je2iwHpuSG2AawCLHl1E08pNAYjPiGfHyR008GuAVVtxMDtcMP+0SL4ln8y8TP61/F98F/0dhdZCannXIuZ0DIFugfSp04c8Sx6p2ak42jnydpe3aRzQ+KruLTEzEYu2SMZlIYQQdx0JXoUQ4iYqmjd6LZlxtdYcTDvIP5b9gwUHF1Ddqzpj24/l8KnD/HH4D2JOxdCrdi+GNBpCq+BWHEs/RvPKzYsTJqVkpbAxfiN96/Ytvc611QpHjhjJezIzIT0d9u0zlj/Zu9eYJ1n0/3s7O6MXLzDQ6Mnz94eaNaFaNWMOZ1GPaEBAhR2GmpCRwMJDCwFwtnOmb2hfvJy8rrvepLNJ/LTrJxYdXkRt79p8cu8nONtLZl4hhBDiWknwKoQQN8n8/fOJ+DUCb2dvxnUex66kXfxx+A+qelZldOvR3FfnPnYn72bWnllsT9xOkHsQdiY7tNZsiN/AvpR9FFgL8HT0ZGyHsYxuMxonO6fi+rXWpYPSIlarsfTJyZOwfz8cOGAEqklJkJho9Kien2HWbIbGjSEszAhMq1aFunWN+ZIuLjf5SQkhhBBCXJkEr0IIcYNYtZUVR1fw3tr3OJh2kBOZJ6jlXYvsgmwSzybiYHagc7XO7E3ZS0JmAjW8anD0zFFMykSob2jx72Zlpk2VNjQNbEqAWwCPNHzk3BBRqxVSUoye02PHjPmmaWlGULp/v5FlNikJLJbSjfP2NnpNAwKMntQWLaBGjXNzTENCJEgVQgghxG3taoLXW75UjhBCVBSF1kK6f9+d1cf+n707j5OjrvM//v5M9/RcPfeRTCYZcpJwCiGCIYpccokoAhrXA9SFdX8rqyu7Krvqrj9/7um1qy4CCroLqyDgIopHBJEAciSQhISEBHJNZjKZ+75nvr8/qrvTM5lJZshMz3eS1/PxqEd1V1dXffs7PdX1ru+3qv6gE/JP0KWLLtXiosW66aybFI1EtbF2o04qPUl5GXnqH+zXrY/dqg21G3TLylt0zcnXaHZWqdTSoqHXdshV1yjU0iJtaJSqdkuv/5m0d28QWhsaDg2mUnBRo6VLpUsuOXg12bKyoPV02bJhFzoCAAA41tHyCoyhf7Bfe1r3aEHBAoXSQuob7FN6WvroXToxpdp727Wzead2NO3Q03uf1lNVT+n1pte1uGixbjjjBkUjUTV0Naihq0F7W/eqIrdC0UhUJdklygxn6g97/qCa9hr1DfaptbdVGaEMRUIRtfa2qqWnRa09rSrMKtTHz/y4KvMrVZBZoJ6BHj2641H9aOOP9LlVn9OtKz+r/M4Bqa7u8ENTU9B9t61N6uwc/QPl5kqLFgVdecvKglA6e0/RnToAACAASURBVPbBaaWlQatqenpqKxoAACDF6DYMTFBrT6t+8/pv9P0Xv6/ugW6lp6Vre+N2VbdXKzOcKZOpe6Bbp886Xe868V360Okf0rKSZYcsp6OvQ9/84ze1Zucave+U9+mTZ39yGj6Nf9a8vkZ/+/jfKj0tXWeVn6UTi0/UW+a+Rbetu0017TV69IOPDrv4UXd/t57Y/YQe3/W4NhzYoGeqnlFXf3A13Mxwps6pOEcnlZykP+77ozYe2DhsXSZTOC087EqzJdklWlS4SJFQRPmZ+erq75L1D+jEtnSd0B7S7E7Ttq1rFW3pUlmnVNQt5fUGw4JQscq604LW0dG2l6HQwRbRsrKDt4DJywuG/PygK+/cucHtY4qLpZwcbnUCAAAgwiswITXtNVp11yrtbtmteXnztKR4iXoGetTY1aiPnvFR1XXWycwUspC+/fy31T3QLZPp/Pnn64olVyg9LV0HOg8onBbWHevv0IHOA6rIrVB1e7VWn7pad77rznHde3OmenzX43q++nktLlqsk0tP1pKiJUoPpatvsE9/2P0H/fzVn+vuDXerJLtE5bnl2lK3Re197cOWsebDa3Txwou1o3GHvrr2q/rVa79SXWedIqGITi07VaeWnaorl1yp+QXz9abZb1IkFJEUnI+66cAmRSNRFWYWqq6zTssKFktVVRo4sF/t+/eou7ZKsxt6FNq1O7jYUWNjEESrqoJ7biZxoZAGiwvVX5Anl5ertPxCZRaVBoF01qyDATV5KCycsVfkBQAAmG6EV+AwmrubdfOvblZNe41OLTtV62rWaeOBjbrjyjt09UlXKzt97Avc9Az0qLWnVbetu00Pbn1Qm+s2D3t95dyV+sY7vqY3R5fq/z78GT383H/pH5Z/Ru9ZelVwUR7ngvHIx/HnoVDQVfRwQ7zFLnkcjQYtfFPczXTIDWlbwza19baprbdNL1S/oC/8/gvD5klPS1d6KF29A70adIPKCmfp0sWX6rtXfFdzcufIOaeqtird/dLdumDBBXrPT96j5p5m5aTnqKu/SzmRHF288GLdtPwmvX3+24O/R/IFjVpapObmYFxTc/ACR01NwTx79x4SSiUF54xWVARBtLg46J574onS/PkHgylBFAAAIKUIrzhuDA4N6vFdj+uhrQ9pfsF8vbXyrVpVuWrM+bv7u/Xe+9+rX7/2a51Vfpa2NWxTZ3+n7rjyDt141o2HvsG5IBBVVwfnMx44EAw1NVJDg7obD8i1tiq9o0u9jQeU09kva20dvXtpKmRnByG2oCAY4o9HjuOPy8uD26cUFR2xG6tzTu974H164JUHhk2/cMGFuu/a+7S3da9eqX9FW+q2qHugW1nhLK2qXKULF1x46AGB3t6gTquq9OpLv9PunS9poL1FeQMhLc89UTk9gwfrurY2GI8WSEOhoDtuaenBULpgQXDP0tmzg2lFRcFnzOJenAAAAL4hvGLaOef0evPrygpnqSKvQl39XdrZvFMdfR3q6OtQW2+btjVsU0NXgyrzK7WkaInOn3++wmlhZYQzhi1ryA3p0R2Pak/LHjX3NGth4UK19bbplfpX9ODWB1XTXqM0S9OQG1I4LazNf75Zm+s2q7O/U+9Z9h7d+MiNenTHo5odna3Ovk7VdtTq9itv143L/1SDnR1qqN2pWZ2Stm4NbklSVxd0K62tlbZsCR6PlJMTBKb4uY3xUFhYeDAcFhbqu9vv1f9WP6ZBkyyUpn43pM+fd6suWXSpvv7cN/XQ9oc1ZNLVy67W377974Irzvb3Dxu2VG/QD1+4U/uadis9dkHa7HCmVs09V+fPv0DzcucEFwZqbZVaWuSam3WgZruspVUZnb3K7R6Qa26WWlsVHhga/e+VkyMVF6srM6SW9EF1Z4ZUXnmKcsoqEi2+29p26qc7HtbZJ5yr+WUnKpKZo4ysqGYVzlMoI1OKRA62DLe3H7xoUfxxY2PQVbeqKgijowmFgrrNzQ1aQ2fPPjiOX9CouPhgHRcXc1EjAACAGYzwiklR21GrVxte1bnzzlV6aHwBoam7SV998qt6aNtD2t2yW+lp6frAaR/QL7f/Uo3djYfMnxXOUvdA97Bp7zrxXSrILFBtR61q2mtUkFmgp6ueHvW9Fy28SNef/hFdWf52vbr1Kf317deovEPK7wmGol5Tfo90RmSe0rt6FenpV0WoULmt3UGLan//IctVUZFUUhKE02XLpFNPPXhV2Hj30ry8cdXHK/Wv6M71d+p9p7xPp806TUu+vUS1HbUqzS5VfVe9Pr/q8+rs79R3X/iu9v3VPpXnlife+3z18/rKk1/RL7b/QvPy5ukL531BWeEs9Qz0aO3etbp/y/3qHezV+095v+557z0Kp4X1QvULuuW3t2jt3rXDypGdnq2uvi5lDUhXl1+o5tpdat6/S3PapcpWaUl7uvK6hpTdM6hon5TbJ5X2pOkEK5QNDEh9fRrs7VH6oFPaRDcZ0WhQX4WFQSvpvHmHDiUlQWjNyOBCRgAAAMcRwivesMauRj249UF989lvalvDNknSwsKF+tgZH9N1p1yn2dHZautt010v3aXnqp9TRihDlfmVOrn0ZK2cu1I3PHyDNtZu1DtPfKcuWXiJnqt+Tv+96b91yaJLdMObblBBZoGikaiikagWFS5UrououWGfNu1+Vq/ueVGtzfv11NbfqGAoojLlKL97SNbcogsLluuMjBMUaetU54EqZbZ3K9LWKYuf+zjaPTIl9UZCUkGBMsrKgxCVkxMM8XAa7z5bXCyddJK0ZImUmTll9ftK/Sv6zxf+U3esv0Nfu+Rr+stz/lKvNryqZd9dpuvfdL1ue+dtSg+l64uPf1Hfeu5bysvI0yff/Endcu4th3S9bexq1Def/aa+uvarOu+E81SYWaiHX31YZTll+tJ5X9Ly8uXqHezV03uf1nPVz+kzKz+jh7c9rEe2P6ITi0/UBfMv0HtPeq8auxt1+7rbFY1E9fb5b9d5J5yn+s56vfnON6uz/+CtXsJpYd1/7f26+sSrpL6+g63D8cfx8dDQwSvtRqNBayoAAAAwCsLrMaZnoEdtvW26f8v9Ksgs0ODQoAaGBlSYVai9rXt11dKrtLBw4RGXMzg0qFDa8CCxr22fHt/1uFp7WlXXWaev/fFr6hno0fLy5Vp9ympV5FXo63/8ul7c/+Ihy1tWskxplqbdzbs01N2t7H4pq1+6823/qssLVgTdb+vrNVBbo3BDU9BtNH6xnebmoOvoWPfBHCkUClrukoeiouHPi4uDcx3nzAlCaV5e0JXVQ70DvcO6R7/7J+/Wz1/9uRYXLdbVy67Wvz3zb7pk0SW69733qiS75LDLuuulu/S5331OHX0dumXlLfrcqs8pNyP3qMv40v6X9EzVM5oVnaVZObN0cunJKs4uPurlAgAAAHGEVw/VtNfo73//94pGolp96mr1DfbpjNlnjBoytjVs00+3/FSDblBP7nlSz1Q9o97B3jGXXZRVpFXzVikvI0/bGrapIq9C51Wep6uWXqWn9j6lF/e/qEdfe1Q56Tl68c9eVDgtrPrOev3j2n/U3RvuVmtva2JZZ1ecrU+d8ym996T3KjMca4Hcu1dNv/uFtqx7VBntXcru7FNlX5by9sWu/trePkbJYsyCls7i4uHnhM6eHbR+xltDs7OHj3NyDp5HGo0e091JnXNas3ON/vyXf66dzTu1qHCRtt+8fdi9Tw9nYGhAfYN9h71SMgAAAOAbwqtnmrubteLOFdrZvHPY9GUly/Tl87+sJ/c8KUlaMWeF/rDnD7pn0z0aGBqQyXTG7DN0wfwLlJWepXPnnasFBQuUGc7UoBtUTXuNstOz9ZUnv6I9LXvU2N2o7PRsbW/cPmw94bSwBoaCK7X+x2X/oQ+d/iGd8/1ztKNph84qP0vfueI7WlS4SPmZ+cH9M3/2M+mee4KW0T17pH37Di4sPf1gq+eiRUFLZ2FhcCXX7OxgnJd38B6YpaVBaKXr6Lh09XfpP577D51TcY4uWHDBdBcHAAAAmFKE12m0o3GHFhUtGtZi9s9P/bNufexWrf3oWuWk56i2o1Z7W/fqE7/8hCQpGolKkjr6OhSykG4++2Z9+i2fVn5mvgoyCyZchoGhAX1v3fcUCUX01sq3alHhIu1o2qE/efBP9HLdyzKZnJzWfHiNLl548aELuO026TvfCS5ONGeOdM450lvfGpwPmpNzTLeAAgAAAEgdwus0GBwa1GfXfFbfePYb+uBpH9Q/XfRPum/LfXpyz5P6/e7f6+yKs/XYRx4b9p7f7fydciO5Wl6+XGmWpu2N2xUJRbSoaNGUlLGzr1NP7H5Cj+96XLOjs/U3q/5mStYDAAAAAONBeE2xrv4urX5gtR7Z/ohOKT1FW+q3JF5bVrJMZ5WfpS+e90UtLVk6jaUEAAAAAL9MJLyGp7owx4Mfv/xjPbL9Ef37Zf+um8++Wb967Vf68M8+rEsWXaIfX/Pj6S4eAAAAAMx4hNdJsKV+i7LTs/XJsz8pM9MVS67Q7k/tVk4kZ7qLBgAAAADHBMLrUerq79K2hm1aWrx02EWaJuM+mwAAAACAAOH1KNz6u1u1ZucaVbVV6aIFF013cQAAAADgmJV25FkwllWVq7R+/3rVddZpWcmy6S4OAAAAAByzCK9H4coTr9QD1z2glXNX6p1L3jndxQEAAACAYxbdho/SNSdfo2tOvma6iwEAAAAAxzRaXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA91IeXs2swMweMLNtZrbVzFamugwAAAAAgJklPA3r/HdJv3bOXWtmEUnZ01AGAAAAAMAMktLwamZ5ks6TdIMkOef6JPWlsgwAAAAAgJkn1d2GF0qql3S3mb1kZt83s5wUlwEAAAAAMMOkOryGJS2XdJtz7kxJnZI+P3ImM7vJzNaZ2br6+voUFxEAAAAA4JtUh9d9kvY5556LPX9AQZgdxjl3h3NuhXNuRWlpaUoLCAAAAADwT0rDq3OuVlKVmS2NTbpI0iupLAMAAAAAYOaZjqsN3yzp3tiVhndK+ug0lAEAAAAAMIOkPLw65zZIWpHq9QIAAAAAZq5Un/MKAAAAAMCEEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALwXTvUKzWy3pHZJg5IGnHMrUl0GAAAAAMDMkvLwGnOBc65hmtYNAAAAAJhh6DYMAAAAAPDedIRXJ+m3ZrbezG6ahvUDAAAAAGaY6eg2vMo5V2NmZZLWmNk259yTyTPEQu1NklRZWTkNRQQAAAAA+CTlLa/OuZrYuE7SzySdPco8dzjnVjjnVpSWlqa6iAAAAAAAz6Q0vJpZjpnlxh9LukTS5lSWAQAAAAAw86S62/AsST8zs/i6/8c59+sUlwEAAAAAMMOkNLw653ZKelMq1wkAAAAAmPm4VQ4AAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4L3wRGY2s5MlXSTpbEmzJWVKapK0XdJTkn7rnOue7EICAAAAAI5vR2x5tcBHzOwFSZsl/YOkuZJaJO2RFJJ0uaQHJdWa2R1mtmDqigwAAAAAON6Mp+V1a2z835I+7JzbNtpMZpYt6VJJ10l62cw+4Zy7Z3KKCQAAAAA4no0nvH5B0oPOOXe4mZxzXZJ+JulnZjZXQessAAAAAABH7Yjh1Tn3wEQX6pzbJ2nfGyoRAAAAAAAjcLVhAAAAAID3xh1ezexkM1s1Yto1ZrbDzJrM7D4zK578IgIAAAAAjncTaXm9Q9JV8SdmtlDSPZI2Svq6pLdK+uaklg4AAAAAAE3sPq+nS/pq0vPrJO1xzl0rSWa2UdL3J7FsAAAAAABIGkd4NbPfxx5GJX3ZzD4be36KpD4zezz2PFNSadLzHzrn/mtSSwsAAAAAOC6N52rDF0iSmTVI+rpz7j4zi0iqlvTXzrmfxF4/XdLvnXMXTmWBAQAAAADHn4l0G35M0jfNbIGkcxWcL/urpNfPkLR9PAsys5CkdZKqnXNXTqAMAAAAAIDj0EQu2PSXktZLulXSiZI+4JxrTXr9ryT9bJzL+pSkrRNYNwAAAADgODbullfn3AFJ7zrM62eOZzlmNlfSOxVc/Okz410/AAAAAOD4NZGW18nyLUmflTQ0DesGAAAAAMxARwyvZvbh2Dmq42Zmi83sbaNMv1JSnXNu/RHef5OZrTOzdfX19RNZNQAAAADgGDSeltdbJL1uZl8xszeNNZOZFZvZB83sEUkvSSofZbZVkq4ys92SfiLpQjO7Z+RMzrk7nHMrnHMrSktLx/VBAAAAAADHrvHcKucMM3u/pJsl/Z2ZdSi42FKDpF5JBZIWSKqU1CzpHkmfcM5Vj7KsWxVc8Elmdr6CW+18aHI+CgAAAADgWDWuCzY55+6TdJ+ZLZJ0saTlkmZLypF0QNKTkp6W9IRzrn+KygoAAAAAOE5N5D6vcs69Lun1yVixc+4JSU9MxrIAAAAAAMe2N3S1YQvMM7NzzSxnsgsFAAAAAECyCYdXM/s/kqol7ZG0VtLS2PSHzOzTk1s8AAAAAAAmGF7N7G8kfUPSnZIulGRJLz8h6f2TVjIAAAAAAGImdM6rpL+Q9CXn3L+Ocu/XVyWdODnFAgAAAADgoIl2G54taf0Yrw1Jyjy64gAAAAAAcKiJhtfXJL19jNfOk/TK0RUHAAAAAIBDTbTb8Lck/aeZ9Ul6IDatzMw+Lukzkm6czMIBAAAAACBN/D6v3zezQklfkvTl2ORHJXVJ+gfn3P9McvkAAAAAAJhwy6ucc/9mZt+TtFJSiaQmSX90zrVOduEAAAAAAJDeQHiVJOdcu6TfTnJZAAAAAAAY1UTv8/pVM7t9jNe+Z2ZfmZxiAQAAAABw0ESvNvwBSWvHeG2tpD85uuIAAAAAAHCoiYbXOZKqx3itJvY6AAAAAACTaqLhtVbS8jFeWy6p/uiKAwAAAADAoSYaXu+X9CUze2fyRDO7QtIXJf1ksgoGAAAAAEDcRK82/CVJZ0h6xMwaJe2XVC6pSMHVh784ucUDAAAAAGCC4dU51yPpEjO7VNIFkoolNUp6zDm3ZgrKBwAAAADAG77P628k/WaSywIAAAAAwKiOGF7NLNs51xV/fKT54/MCAAAAADBZxtPy2m5mK51zz0vqkOSOMH/o6IsFAAAAAMBB4wmvH5P0euzxR6ewLAAAAAAAjOqI4dU59yNJMrN0Sa9J2uWcq5nqggEAAAAAEDeR+7wOSnpc0klTVBYAAAAAAEY17vDqnBuStEPSrKkrDgAAAAAAh5pIy6sk/Z2kL5nZaVNRGAAAAAAARjPR+7x+QVKxpA1mVi3pgEZcfdg5d/YklQ0AAAAAAEkTD6+bYwMAAAAAACkzrvBqZlmSrlAQXGsl/c45d2AqCwYAAAAAQNwRw6uZLZT0O0nzkya3mdn7nHO/naqCAQAAAAAQN54LNv2rpCFJb5OULekUSS9Jun0KywUAAAAAQMJ4wutKSV9wzj3tnOtxzm2V9GeSKs2sfGqLBwAAAADA+MJruaSdI6a9LskkzZ70EgEAAAAAMMJ47/PqjjwLAAAAAABTY7y3yvmNmQ2MMv2xkdOdc2VHXywAAAAAAA4aT3j98pSXAgAAAACAwzhieHXOEV4BAAAAANNqvOe8AgAAAAAwbQivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4L2UhlczyzSz581so5ltMTPuIQsAAAAAOKJwitfXK+lC51yHmaVLesrMfuWcezbF5QAAAAAAzCApDa/OOSepI/Y0PTa4VJYBAAAAADDzpPycVzMLmdkGSXWS1jjnnkt1GQAAAAAAM0vKw6tzbtA5d4akuZLONrNTR85jZjeZ2TozW1dfX5/qIgIAAAAAPDNtVxt2zrVIekLSZaO8dodzboVzbkVpaWnKywYAAAAA8EuqrzZcamYFscdZki6WtC2VZQAAAAAAzDypvtpwuaQfmVlIQXC+3zn3ixSXAQAAAAAww6T6asObJJ2ZynUCAAAAAGa+aTvnFQAAAACA8SK8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4L6Xh1czmmdnvzWyrmW0xs0+lcv0AAAAAgJkpnOL1DUi6xTn3opnlSlpvZmucc6+kuBwAAAAAgBkkpS2vzrn9zrkXY4/bJW2VVJHKMgAAAAAAZp5pO+fVzOZLOlPSc9NVBgAAAADAzDAt4dXMopIelPRp51zbKK/fZGbrzGxdfX196gsIAAAAAPBKysOrmaUrCK73OuceGm0e59wdzrkVzrkVpaWlqS0gAAAAAMA7qb7asEn6gaStzrlvpHLdAAAAAICZK9Utr6skfVjShWa2ITZckeIyAAAAAABmmJTeKsc595QkS+U6AQAAAAAz37RdbRgAAAAAgPEivAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeC+l4dXM7jKzOjPbnMr1AgAAAABmtlS3vP5Q0mUpXicAAAAAYIZLaXh1zj0pqSmV6wQAAAAAzHyc8woAAAAA8J6X4dXMbjKzdWa2rr6+frqLAwAAAACYZl6GV+fcHc65Fc65FaWlpdNdHAAAAADANPMyvAIAAAAAkCzVt8r5saQ/SlpqZvvM7OOpXD8AAAAAYGYKp3JlzrkPpHJ9AAAAAIBjA92GAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3iO8AgAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAAAAB4j/AKAAAAAPAe4RUAAAAA4D3CKwAAAADAe4RXAAAAAID3CK8AAAAAAO8RXgEAAAAA3gtPdwEAAEDqDQ0NaWBgQOnp6TKz6S7OpHLOqa2tTfv371dzc7N6e3uHDX19fRoYGNDg4OCYY0nKyspSZmamsrKyEkNpaakqKytVXFys9PT0af6kwBvnnFNvb6+6u7vV2dmp2tpa+SN2bQAAGaBJREFU7d+/X1VVVdq3b5/a29uVlpamzMxMZWRkKDMzU7m5uYcd8vLyFA4TLzB1+HYBADDDOefU3t6u6upq1dTUqLm5Wa2trWptbVVLS4saGhpUX1+fGNfX16uxsTER0tLS0pSenq5IJKL8/HxlZ2cnhpycHEWjUeXm5ioajaqgoGDMobCwUPn5+ZMe6pxzam1t1TPPPKPnn39eu3btUnNzszo6OtTe3q6uri51d3eru7tbXV1d6urq0sDAwKSWYTQFBQVauHChTj31VM2fP1+5ubkqLi7WKaecolNOOUU5OTlTXgZgpG3btumJJ57Qyy+/rAMHDqijo0OdnZ2JcfLj+DZgpFAopNzcXA0NDSUO+oxHJBLRaaedpnPPPVdvfvObVVxcnNg+5OTkaM+ePWpqalJzc7Oam5vV0tKiwcFBRSKRxJCTk6OKigoVFxcrLy9PeXl5Ki4uVnZ29jF3oA0TZ8651K7Q7DJJ/y4pJOn7zrl/Ptz8K1ascOvWrUtJ2RCoqalRQ0ODCgsLExsLpF5/f78GBgYSw9DQkEKh0LAhLS1NzjkNDQ3JOTdsGGuaJOXk5CgzM5MfgRlscHBQfX19iVak+Hi0acnjtLS0xM5A8pCbm6u0NM4k8VVXV5e2b9+umpoaVVdXa8+ePdq6datee+21RBDt6+sb9b1mpuLiYpWUlKi0tFSlpaWJx1lZWRoYGEhsb3p6etTa2poIgZ2dnerq6lJHR0ciKMZ3Ng8nJycnscM6e/ZsLV68WLNmzdKiRYt0+eWXq7S0dNj8AwMDam9vV1tbm1pbW9XQ0KD169fr2Wef1fr161VbW5vYeTYzzZ07V8XFxYlAnZ2drczMzGGhu7i4WOXl5SoqKlJGRsawIRKJKBwOKxwOKxQKjTp2zqmnpycRiuOP43+DhoYG1dXV6bXXXtPLL7+smpqaQ+ohEokkgn9JSYmWLVumJUuWKCcnR5FIJFGW5MdjTcvMzFR5ebkikcgh6xkaGlJ/f7/6+voS4+THh5s21mudnZ1qb29Xe3u76urqVFNTo6amJg0ODiZaqJ1zys3NHdYyl5GRofz8fBUWFqqoqGjUcTQaHfa5srKyxjzg4ZxTf3+/ent71dPTM+q4t7dXGRkZiQMtOTk5ysnJSfQqGDnEfx+Tfyfj6xg51NXVqba2NlEv8WFwcFCZmZmJdcXXGz+IU1RUpPz8/Cn/nW1ra9P69ev18ssva926ddq4caM2bdokScrPz9ecOXOUm5t7SDmT6yn+PzN79myVl5eroqJC5eXlw8oeb6WNbwfGGvbt25f43+3u7j5i+c1MoVBoXAeb4vWdlpaWGMxsUp+npaUltg3p6emJxyOfxx+HQqFh3620tDSVlpbqhBNO0IIFC3TmmWcedUt0f3+/6uvrE2G/paVFra2t6ujoGHbALv44Po4fIOjr69P111+vz3zmM0dVjqlkZuudcyvGM29KW17NLCTpu5LeIWmfpBfM7OfOuVdSWQ4c3l133aUvfvGLkoKj8e94xzt03XXXqbCwMLGBi0ajmjt3rkpKSmRmiR+y5MCV3P0q+cfucM/HO08kElFmZqbS09MTQS4cDis/P1/z5s1L/BiGw+HD/nDEN8atra3au3evqqqq1NjYqObmZjU1NSU2EP39/RoaGkoM8R+90YaxXnPODdsIJm8ABwcHExul+Linp2dK/86hUEh5eXnKz89XaWmpcnNzh3WNG627XDQaTQzxncZoNKrMzMxE16Pknbz4BjXeIlRdXa3u7m4NDQ0l/pbxxyPH45021mvJO0bJO1bxIS8vT+Xl5Vq4cKEKCwtVVlamk046SWVlZWP+0AwMDCSOWMeH5B38zs5O9fb2Jv4HRh6AONzQ3d2t5uZmtbW1DevaONb4SOHhjYhGowqHw4f8kMeHaDSqoqIiFRUVqbi4+JBxYWFh4juVl5ennJwcZWRkJP4Hxvr7xf/H09PTh/2N4n+H5IOsyX/vsbYVyTulo+2kjvXa0c4jSUVFRYmAGI1G39COa09PjzZt2qTXX39dGzZs0Jo1a7Rp06Zhf/NQKKTFixdryZIlidaNkpISVVRUaM6cOSouLlZ+fr7y8/MVjUYVCoWO8ttxkHNOnZ2diZ2o5CF55yo+VFVV6aGHHlJDQ4Occ0pLS9OZZ56pjo6ORFjt6uoadV2LFi3SypUrNW/ePJWUlOiss87Sueeeq6ysrEn7PIczkYO3Q0NDia6Xmzdv1quvvqrW1tbETn1tba2efPJJ3XvvvW+4PGamzMzMUX9fpkJGRoai0ahKS0tVUVGhuXPnJnbY4zvtHR0dw4JkU1OTdu/eraampkTYHY9QKJT4rUlLSxu2zFQ3tEyWrKwszZs3TwUFBcN+90Oh0LADy6FQaNi2L35wOf4/lfzbGv99TT6wEldRUaHTTjtNq1ev1urVqzV//vxJC8/x715mZqZKSkqOOH9fX5927949bJvQ1tamyspKlZWVqbCwUIWFhYkDp8kHYOIhOP6b2NraqqamJtXX16urq+uQ/azJfB4/MNzZ2Tnmb/nI58l/y8HBwWGt1CeddJIuv/xyLV++XCUlJYk6dM4l9jnjvWTiPWWamprU2NiYGFpbW8f194nvo8f/jwoLC1VSUqKMjAwVFxcf1d/fJylteTWzlZL+wTl3aez5rZLknPunsd5Dy2vq7dixQxs3blRzc7N27dqlu+66SwcOHBh1XjPz+kclfq7GyKG9vV0NDQ2HPSqYnp6eaDmIRCKHPUo3ntckJXawR24E09LSVFhYOKzrXW5u7rDWgfhBguRhaGgosd6RR/7GmuacSwTKtrY2tbS0qL6+Xu3t7cN+DJOH/v7+o/5bZGRkaO7cucrJyUm0Gh9uPJ55xpo3LS1N/f39w47Oj3zc0tKimpqaUQ8SxLstxcsaD6yTcUBhtAMY4XBYGRkZKioqUl5eXiJsj9Yac7iWmvGMBwcH1dbWlhhaW1sTj9vb2xPfq+Qh/n3r7OxM/KjGx52dnUddJ8eqeCt3PMzn5eUdstM62rBz5041NDRICr4vq1at0tve9jadfvrpmjdvnubMmaPy8vIZd77lwMCANm3apAcffFBPPPGE5syZo4KCgkPqKC8vTwUFBTr99NNVVlY23cWedOPtMTFyWldXl6qrq9XZ2TnstyW+DYxEIomu3/HxaNMO91r8cXp6urKzs4/6O+acU0dHR6KbaHzc2dmZ+Fyj/e4MDQ0d0pp7uHEkEkm0Csa7xXZ2dqq/v3/U/7Hk3+v4b2R6evqw1vn48ktKSlReXq6MjIxE3cQDaE9PzyHdcONBq7GxUTU1NaqqqlJbW9shv/3Jv8/xwBP/ferp6dHQ0FBinyAeSOIHlJPHhYWFOuuss7R06VLNnz9/cr6kOCqtra3as2ePNmzYoNtvv10vvvjiEfcfQqFQ4oBjcXHxsAPDJSUlmjVr1rD9xPz8fOXm5iYCayQSmdG96SbS8prq8HqtpMucc38ae/5hSec45z451nsIr9Ovr69P1dXVamtrS7SktbW1qaqqSg0NDYmN/sguFcldsJKH8Uw73DxpaWnq6+tTT0/PIa289fX1OnDgwLAjk8lDfFo0GlVZWVniByEajaqyslKVlZUqKSlJtDLP5A3BZBocHExc0CG51TE+dHd3Jw4MxH9Q49174ufMFRUVeVefg4ODqq+vTwTZrVu3qqmpadjOz8DAwLBW5vgQD7fJ3a7iLY0juxol/0/4VgdHK97SEt8pjQfieGtavKvykQ5ChMPhRLfV+NDf35+or+TxkbYZI3dMk3dQx3ptIvOMNe/Q0FCidaC+vj5xYCB5PDQ0NGo3xuShsLBQ1157rZYtW6aFCxemrJURADD5BgYGEj0x4r9vzjnNnTtXZWVlysvLO+73OX0Or9dJunREeD3bOXfziPluknSTJFVWVp61Z8+elJURAAAAAJAaEwmvqb46xz5J85Kez5V0yBUOnHN3OOdWOOdWjLyoAwAAAADg+JPq8PqCpCVmtsDMIpJWS/p5issAAAAAAJhhUnq1YefcgJl9UtJvFNwq5y7n3JZUlgEAAAAAMPOkNLxKknPuUUmPpnq9AAAAAICZizvSAwAAAAC8R3gFAAAAAHiP8AoAAAAA8B7hFQAAAADgPcIrAAAAAMB7hFcAAAAAgPcIrwAAAAAA7xFeAQAAAADeI7wCAAAAALxHeAUAAAAAeI/wCgAAAADwHuEVAAAAAOA9wisAAAAAwHuEVwAAAACA98w5N91lOCwzq5e0Z7rLcYwrkdQw3YU4TlDXU486Th3qOnWo66lHHacG9Zw61PXUo44nxwnOudLxzOh9eMXUM7N1zrkV012O4wF1PfWo49ShrlOHup561HFqUM+pQ11PPeo49eg2DAAAAADwHuEVAAAAAOA9wisk6Y7pLsBxhLqeetRx6lDXqUNdTz3qODWo59ShrqcedZxinPMKAAAAAPAeLa8AAAAAAO8RXmcgM5tnZr83s61mtsXMPhWbXmRma8xsR2xcGJu+zMz+aGa9ZvbXI5ZVYGYPmNm22PJWjrHOy8zsVTN7zcw+nzR9rZltiA01Zva/U/nZU22a6vouM6szs80jpl8XK8OQmR0zV7abrDo2s6VJ38UNZtZmZp8eY51jfZ9/YGYbzWxT7G8VnerPn0qe1TXbjsmva7YdesPb57+KLWOzmf3YzDLHWOf1seXuMLPrk6b/Orbt2GJm3zOz0FR+9lTypZ7NLHfE/0KDmX1rqj9/Kk1TXf/azFrM7Bcjpn8ytt12ZlYyVZ851Sa5jj8Vq98tY22XY/Mdl/scU8Y5xzDDBknlkpbHHudK2i7pZEn/Kunzsemfl/Qvscdlkt4s6auS/nrEsn4k6U9jjyOSCkZZX0jS65IWxubZKOnkUeZ7UNJHprt+ZnJdx147T9JySZtHTD9J0lJJT0haMd1142Mdj/jO1iq4b9i4v8+S8pLm+0Z8/cfK4FNdj5iPbcdR1nXsdbYdb6COJVVI2iUpK/b8fkk3jLK+Ikk7Y+PC2OPC2Gt5sbHFvs+rp7t+jsV6HjHfeknnTXf9zOS6jr12kaR3SfrFiOlnSpovabekkumuGw/r+FRJmyVlSwpL+p2kJaOs77jd55iqgZbXGcg5t98592LscbukrQo2VO9WEJAUG78nNk+dc+4FSf3JyzGzPAU7Oz+IzdfnnGsZZZVnS3rNObfTOdcn6SexdSUvK1fShZKOqdaTaahrOeeelNQ0yvStzrlXJ+Nz+WSy6niEiyS97pzbM8prY36fnXNtkmRmJilL0jF1UQCf6jqObcek1TXbjqOr47CkLDMLK9gZrRllnkslrXHONTnnmiWtkXRZbNltScuJ6BjadvhUz3FmtkRBqFh7lB/PK9NQ13LOPSapfZTpLznndh/VB/LQJNbxSZKedc51OecGJP1B0tWjrPK43eeYKoTXGc7M5is4OvacpFnOuf1S8M+pYMN+OAsl1Uu628xeMrPvm1nOKPNVSKpKer4vNi3Z1ZIeS/oBP+akqK6Pa0dZx8lWS/rxGK8d9vtsZncraN1aJunbE1jnjOJDXcew7Ri/w9X1ce1o6tg5Vy3pa5L2StovqdU599tRZj3StuM3kuoUBIEH3uBH8ZoP9RzzAUn3OeeO2Z39FNX1ce0ot82bJZ1nZsVmli3pCknzRpmPfY5JRnidwWJ94x+U9Ok3uOMXVtDF7Dbn3JmSOhV0lThkVaNMG/mD8QEdwztVKazr49Yk1HF8ORFJV0n66VizjDIt8X12zn1U0hwFR2Pf/0bL4TNf6jqGbcf4lnOkuj5uHW0dx85te7ekBQr+93PM7EOjzTrKtORtx6UKuiRmKOhNcEzxpZ5jjukDOSms6+PW0daxc26rpH9R0DPg1wq6Aw+MtqrR3p60nGN+n2OyEV5nKDNLV/BPd69z7qHY5ANmVh57vVzBEeDD2Sdpn3PuudjzByQtj53MHr8gwidi8yUfTZqrpO4nZlasoFvEL4/2c/koxXV9XJqkOo67XNKLzrkDsfdO6PssSc65QUn3SbrmjX4mX/lU12w7JrWuj0uTVMcXS9rlnKt3zvVLekjSuWZ2TlIdX6XxbTt6JP1cI7rHz3Q+1bOZvUlS2Dm3flI+nGdSXNfHpcnaNjvnfuCcW+6cO0/BKRs72OeYeuHpLgAmLtY3/geStjrnvpH00s8lXS/pn2Pjhw+3HOdcrZlVmdnS2PlQF0l6xTlXJemMpPWFJS0xswWSqhUc8fyTpEVdp+BE/56j/3R+SXVdH48mq46TDGvJG+/3OVaORc6512KP3yVp2xv+YB7ypa6T3s+2Y5Lq+ng0iXW8V9JbYl3/uhVsn9fFDjYmf5+LJP1jrFVLki6RdGusBSfXObc/9p2/QsfQuZi+1HPSco7Z3hqpruvj0WRum82szDlXZ2aVkt4raWXsPG32OaaS8+CqUQwTGyS9VUGXg02SNsSGKyQVS3pM0o7YuCg2/2wFR37aJLXEHsevjHiGpHWxZf2vRrmiX2y+KxRcke11SX834rUnJF023fVyDNX1jxWco9Ife//HY9Ovjj3vlXRA0m+mu348rONsSY2S8o+wzkO+zwp6ojwt6WUF57Lcq6QrAR4Lgy91nfQa247JrWu2HW+8jr+sYMdxs6T/lpQxxjo/Jum12PDR2LRZkl6IlWOLgvPWwtNdP8daPSe9tlPSsumul2OortcquCZHd+z9l8am/2Xs+YCClsLvT3f9eFjHayW9oqDL8EWHWedxuc8xVYPFKhAAAAAAAG9xzisAAAAAwHuEVwAAAACA9wivAAAAAADvEV4BAAAAAN4jvAIAAAAAvEd4BQAc88zMjWM438xuiD2Oprh8t5jZ75Oe35BUriEzazWzTWb2LTNblMqyJZUpy8zqzOxt07F+AADC010AAABSYGXS4yxJj0v6f5J+mTT9FQX36VwpqStVBYsF5c9J+vAoL1+o4P6LUUmnSPozSTea2bXOuV+lqoyS5JzrNrNvS/qKpPNTuW4AACRxn1cAwPElFhbbJX3UOffDaS6OzOxGSV+SVOliP8pmdoOkuyXlOuc6kubNkPQLSSskzXfOtaa4rPMk7ZH0Jufcy6lcNwAAdBsGACBmZLdhM5sfe77azO42szYz22dmH4q9/lkzqzGzejP7FzNLG7G8U83sl2bWHht+amazR6z2ekkPuXEcTXbO9Uq6WVKBpA8krecWM3sh1r34gJk9YmaLk17/i9j6h3WHNrMLYp/v9Njzq8xsvZl1mlmzmT1nZm9PWn+VpBckfWRcFQoAwCQivAIAcGT/Imm/pGskrZX0IzP7uqSzJX1M0rckfVbS++JviIXHpyVlKugSfIOCrr+PmJnF5smRdI6kZ8ZbEOfcNkn7JL0lafJcSd+R9G5JN0oKSXrazPJjr9+r4FSha0cs7gZJLzrnNsXOpX1AQZfqd0n6oIJW3qIR73lG0sXjLS8AAJOFc14BADiyx51zfytJZvacghB4laRlzrlBSb82s3dLulrST2Lv+XtJtZIud871xd67SdI2SVcoON/2TQp+izdPsDz7JM2KP3HO/VX8sZmFJK2RVKcgzP6Xc67FzB6U9FFJP4zNF1UQxj8fe+uZktqdc3+TtJ5HR1n3Rkk3m1mmc65nguUGAOANo+UVAIAjeyz+wDnXJqle0h9iwTXuNUkVSc8vlvQzSUNmFjazsKRdknYrOGdVkuJdiBsmWB4b9sTsLWa2xswaJQ0ouOBUVNKJSbP9QNLbzGxh7Pn7FATn/4k9f1lSvpn9yMwuibUKj6ZBQctu6QTLDADAUSG8Av+/vfsH7auKAjj+PeDQODl00WhpAx2EVJBapKhTKal0EERBunVpFhexYwd1cTNowUHbNLSQoahYpCSKxmxCiwg6RcROTRWSooOKFTwO5/3k8fK/5Jf8hu8Hfvx47913310P55x7JWljv3Wu761xb0/rei+1i/A/nd8I8Fgzpjf+7y2uZxj4FSAi9gFfUAHtOPAMcITKvLbXMw/8TJUKQ2Vhr2XmXYDMXKAytSNUxnUpIqYjohuk9ta6B0mSdpBlw5Ik9cddKvN6YZVnS60xUBswdYPhVUXE41SP6zfNrRPAg8ALmflHM+YBOr2qmZkRMQmciYgrwLPA850x14HrTa/sSaqX9zzwSmvYQ521S5K0IwxeJUnqj6+AUeDbdXYSXmj+D1DlxOtqjsp5jwp0e721Q8C/VLlwT68kuGsKeAuYBG5TvbErNEfwTDc7DR/tPN4PLGfm8kbrlSRpOxm8SpLUH28AN6hM5iSVbR0GjgNTmTmfmbci4g5wGPh6lTmORMRfVGZ1lCoL3g+81DrjdY7qQb0UERepHY3PskomNzMXI2KWyqq+3e7ZjYhxKlCdBRaBg8DLwOXONE+xhd2RJUnaLva8SpLUB5n5I3WczZ/AB8AM8CbVM/pTa+gndMp3W+ao8uBr1BE4XwJPZOZM6zs/UP2rT1NH25yigs7fV8xWPm3+L3Xuf09twvQO1UN7DviQ6tsF/i9HPgZ8vMbckiT1TWziTHRJktQnEfEkcBN4NDN/2YHvXQUezszn7uPdMeAq8Eivv1aSpJ1i5lWSpF2Umd8BnwOv9vM7EXEoIk4DLwLv3uc0rwETBq6SpN1gz6skSbvvdWCsz9/4jDq+5/3M/GirL0fEEFXCPLHdC5MkaTMsG5YkSZIkDTzLhiVJkiRJA8/gVZIkSZI08AxeJUmSJEkDz+BVkiRJkjTwDF4lSZIkSQPP4FWSJEmSNPD+A5k1g9C2T/kAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -1.035067\n",
      "p-value                          0.740261\n",
      "#Lags Used                       0.000000\n",
      "Number of Observations Used    755.000000\n",
      "Critical Value (1%)             -3.439041\n",
      "Critical Value (5%)             -2.865376\n",
      "Critical Value (10%)            -2.568813\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(ts_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Dickey-Fuller test results confirm that the series is still non-stationary. Again the test statistic is larger than the 5% critical value and the p-value larger than 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposition = seasonal_decompose(ts_log, freq = 52)\n",
    "\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(411)\n",
    "plt.plot(ts_log[-80:], label='Original')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend[-80:], label='Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal[-80:],label='Seasonality')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual[-80:], label='Residuals')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time series are stationary if they do not have trend or seasonal effects. We are going to use the difference transform to remove the time series' dependence on time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Seasonality')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_log_diff = ts_log - ts_log.shift()\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(ts_log_diff,color='green')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Time(Days)',fontsize=15)\n",
    "plt.ylabel('$(Dollar)',fontsize=15)\n",
    "plt.title('Seasonality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -1.110267e+01\n",
      "p-value                        3.830629e-20\n",
      "#Lags Used                     7.000000e+00\n",
      "Number of Observations Used    7.470000e+02\n",
      "Critical Value (1%)           -3.439134e+00\n",
      "Critical Value (5%)           -2.865417e+00\n",
      "Critical Value (10%)          -2.568834e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ts_log_diff.dropna(inplace=True)\n",
    "test_stationarity(ts_log_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above graph shows how the rolling mean and rolling standard deviation are comparitively consistent over time after the time series transformation. We can proceed to use this transformed data for training our  models and forecasting the stock price.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part A\n",
    "\n",
    "### ARIMA\n",
    "\n",
    "\n",
    "An autoregressive integrated moving average model is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables. The model's goal is to predict future securities or financial market moves by examining the differences between values in the series instead of through actual values.\n",
    "\n",
    "The componenets of ARIMA model are\n",
    "1. Autoregression (AR) refers to a model that shows a changing variable that regresses on its own lagged, or prior, values\n",
    "2. Integrated (I) represents the differencing of raw observations to allow for the time series to become stationary, i.e., data values are replaced by the difference between the data values and the previous values\n",
    "3. Moving average (MA) incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations\n",
    "\n",
    "Each of the above component functions as a parameter with the standard notation of ARIMA(p,d,q) which is defined as\n",
    "1. p: the number of lag observations in the model; also known as the lag order\n",
    "2. d: the number of times that the raw observations are differenced; also known as the degree of differencing\n",
    "3. q: the size of the moving average window; also known as the order of the moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Predicted vs Expected Values...\n",
      "\n",
      "\n",
      "predicted=1632.663588, expected=1641.539673\n",
      "predicted=1644.849398, expected=1665.270264\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = out_full[ind]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1669.031243, expected=1696.349731\n",
      "predicted=1700.489369, expected=1695.749634\n",
      "predicted=1698.820269, expected=1689.300415\n",
      "predicted=1692.183737, expected=1683.989990\n",
      "predicted=1686.888445, expected=1689.119995\n",
      "predicted=1692.374842, expected=1698.749756\n",
      "predicted=1702.171907, expected=1704.860229\n",
      "predicted=1708.177573, expected=1723.859619\n",
      "predicted=1727.675974, expected=1715.970093\n",
      "predicted=1718.848621, expected=1723.789795\n",
      "predicted=1727.231550, expected=1734.779907\n",
      "predicted=1738.342584, expected=1750.079956\n",
      "predicted=1753.834203, expected=1730.219727\n",
      "predicted=1732.758222, expected=1715.669922\n",
      "predicted=1718.333175, expected=1663.149902\n",
      "predicted=1664.222501, expected=1691.089600\n",
      "predicted=1695.054875, expected=1660.510376\n",
      "predicted=1662.555927, expected=1701.450073\n",
      "predicted=1705.536674, expected=1699.799927\n",
      "predicted=1702.807793, expected=1713.780273\n",
      "predicted=1717.218071, expected=1693.960327\n",
      "predicted=1696.556376, expected=1699.730225\n",
      "predicted=1702.922213, expected=1710.629639\n",
      "predicted=1713.956251, expected=1739.020386\n",
      "predicted=1742.845071, expected=1743.070068\n",
      "predicted=1746.339143, expected=1754.999634\n",
      "predicted=1758.497612, expected=1796.619873\n",
      "predicted=1800.975616, expected=1813.029907\n",
      "predicted=1816.832826, expected=1822.489868\n",
      "predicted=1826.164697, expected=1843.929688\n",
      "predicted=1847.986435, expected=1842.919678\n",
      "predicted=1846.390891, expected=1812.970215\n",
      "predicted=1815.608194, expected=1813.699951\n",
      "predicted=1817.127359, expected=1802.000244\n",
      "predicted=1805.040980, expected=1829.239868\n",
      "predicted=1833.361460, expected=1863.609741\n",
      "predicted=1868.061545, expected=1808.000122\n",
      "predicted=1810.134035, expected=1817.270142\n",
      "predicted=1820.876728, expected=1779.219604\n",
      "predicted=1781.711706, expected=1777.439697\n",
      "predicted=1780.654691, expected=1797.170044\n",
      "predicted=1800.874220, expected=1834.329590\n",
      "predicted=1838.560468, expected=1823.290405\n",
      "predicted=1826.430687, expected=1847.749634\n",
      "predicted=1851.703204, expected=1862.479736\n",
      "predicted=1866.278868, expected=1886.519775\n",
      "predicted=1890.604039, expected=1898.520264\n",
      "predicted=1902.388297, expected=1886.300293\n",
      "predicted=1889.617437, expected=1896.199585\n",
      "predicted=1900.014697, expected=1919.650391\n",
      "predicted=1923.821436, expected=1882.619751\n",
      "predicted=1885.465446, expected=1886.519775\n",
      "predicted=1890.165311, expected=1882.220337\n",
      "predicted=1885.674909, expected=1876.709595\n",
      "predicted=1880.117393, expected=1883.419800\n",
      "predicted=1887.078162, expected=1904.899658\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1908.908648, expected=1902.899658\n",
      "predicted=1906.440820, expected=1905.390137\n",
      "predicted=1909.027222, expected=1927.680176\n",
      "predicted=1931.764044, expected=1932.819824\n",
      "predicted=1936.583531, expected=1998.100464\n",
      "predicted=2003.259742, expected=2002.380127\n",
      "predicted=2006.348924, expected=2012.709961\n",
      "predicted=2016.846228, expected=2039.509888\n",
      "predicted=2044.053434, expected=1994.819824\n",
      "predicted=1997.940399, expected=1958.310425\n",
      "predicted=1961.291501, expected=1952.069458\n",
      "predicted=1955.639617, expected=1939.010254\n",
      "predicted=1942.361876, expected=1987.149902\n",
      "predicted=1991.956670, expected=1990.000244\n",
      "predicted=1993.847978, expected=1989.870239\n",
      "predicted=1993.669983, expected=1970.189575\n",
      "predicted=1973.515575, expected=1908.030396\n",
      "predicted=1910.081067, expected=1941.050049\n",
      "predicted=1945.333732, expected=1926.420410\n",
      "predicted=1929.710757, expected=1944.299683\n",
      "predicted=1948.239666, expected=1915.009644\n",
      "predicted=1918.048669, expected=1934.359619\n",
      "predicted=1938.245407, expected=1974.549683\n",
      "predicted=1978.926500, expected=1974.850098\n",
      "predicted=1978.554226, expected=2012.979736\n",
      "predicted=2017.427681, expected=2002.999878\n",
      "predicted=2006.644821, expected=2004.360352\n",
      "predicted=2008.182315, expected=1971.309692\n",
      "predicted=1974.493261, expected=1952.760254\n",
      "predicted=1956.062372, expected=1909.420288\n",
      "predicted=1912.063033, expected=1889.650146\n",
      "predicted=1892.616438, expected=1864.420288\n",
      "predicted=1867.136919, expected=1870.320312\n",
      "predicted=1873.743644, expected=1755.249878\n",
      "predicted=1755.799668, expected=1719.360229\n",
      "predicted=1721.110031, expected=1788.609741\n",
      "predicted=1793.444147, expected=1760.949585\n",
      "predicted=1763.265111, expected=1819.960083\n",
      "predicted=1824.037857, expected=1831.730103\n",
      "predicted=1835.051770, expected=1770.720337\n",
      "predicted=1772.691528, expected=1764.029785\n",
      "predicted=1766.838957, expected=1789.299805\n",
      "predicted=1792.718514, expected=1768.700073\n",
      "predicted=1771.290480, expected=1664.200195\n",
      "predicted=1664.534745, expected=1782.170166\n",
      "predicted=1784.056168, expected=1642.809814\n",
      "predicted=1651.422353, expected=1538.879761\n",
      "predicted=1541.997046, expected=1530.420410\n",
      "predicted=1532.673700, expected=1598.010132\n",
      "predicted=1599.914911, expected=1665.530029\n",
      "predicted=1668.391153, expected=1665.530029\n",
      "predicted=1668.118992, expected=1627.800049\n",
      "predicted=1630.127684, expected=1642.809814\n",
      "predicted=1645.361275, expected=1755.490112\n",
      "predicted=1759.035513, expected=1754.910034\n",
      "predicted=1757.754759, expected=1712.430054\n",
      "predicted=1714.914209, expected=1636.849976\n",
      "predicted=1638.300520, expected=1631.169922\n",
      "predicted=1633.528938, expected=1599.010132\n",
      "predicted=1600.843785, expected=1619.440186\n",
      "predicted=1622.119615, expected=1593.409668\n",
      "predicted=1595.392626, expected=1512.289673\n",
      "predicted=1512.865591, expected=1495.460205\n",
      "predicted=1497.114803, expected=1516.729614\n",
      "predicted=1519.261101, expected=1502.060059\n",
      "predicted=1503.756722, expected=1581.330200\n",
      "predicted=1584.979179, expected=1581.419922\n",
      "predicted=1583.643079, expected=1677.750244\n",
      "predicted=1681.808915, expected=1673.569824\n",
      "predicted=1676.010790, expected=1690.169800\n",
      "predicted=1692.958834, expected=1772.359985\n",
      "predicted=1776.485388, expected=1668.400269\n",
      "predicted=1671.049128, expected=1699.189697\n",
      "predicted=1701.606372, expected=1629.130249\n",
      "predicted=1632.307806, expected=1641.030273\n",
      "predicted=1643.355501, expected=1643.239990\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1645.675681, expected=1663.540161\n",
      "predicted=1665.811400, expected=1658.380249\n",
      "predicted=1660.945359, expected=1591.909790\n",
      "predicted=1594.958937, expected=1520.909912\n",
      "predicted=1523.006422, expected=1551.479614\n",
      "predicted=1553.478550, expected=1495.080200\n",
      "predicted=1497.571582, expected=1460.829712\n",
      "predicted=1462.852852, expected=1377.449951\n",
      "predicted=1378.787036, expected=1343.960083\n",
      "predicted=1345.132180, expected=1470.899902\n",
      "predicted=1472.797425, expected=1461.640015\n",
      "predicted=1463.552028, expected=1478.019897\n",
      "predicted=1479.881801, expected=1501.969849\n",
      "predicted=1503.882414, expected=1539.130005\n",
      "predicted=1541.163951, expected=1500.279907\n",
      "predicted=1502.431881, expected=1575.389771\n",
      "predicted=1576.768140, expected=1629.510132\n",
      "predicted=1631.675980, expected=1656.579834\n",
      "predicted=1658.969597, expected=1659.420410\n",
      "predicted=1661.833145, expected=1656.219604\n",
      "predicted=1658.623868, expected=1640.560059\n",
      "predicted=1642.920897, expected=1617.210083\n",
      "predicted=1619.482776, expected=1674.560425\n",
      "predicted=1676.876365, expected=1683.780396\n",
      "predicted=1686.235774, expected=1693.219849\n",
      "predicted=1695.699603, expected=1696.200073\n",
      "predicted=1698.694903, expected=1632.169678\n",
      "predicted=1634.578972, expected=1640.020264\n",
      "predicted=1642.334132, expected=1654.929688\n",
      "predicted=1657.268409, expected=1670.569580\n",
      "predicted=1672.952335, expected=1637.889893\n",
      "predicted=1640.290411, expected=1593.880127\n",
      "predicted=1596.047220, expected=1670.430176\n",
      "predicted=1672.369753, expected=1718.729858\n",
      "predicted=1721.260340, expected=1626.229736\n",
      "predicted=1629.197301, expected=1633.310303\n",
      "predicted=1635.556795, expected=1658.810425\n",
      "predicted=1660.967793, expected=1640.259644\n",
      "predicted=1642.739702, expected=1614.370117\n",
      "predicted=1616.812544, expected=1588.219604\n",
      "predicted=1590.544134, expected=1591.000000\n",
      "predicted=1593.142911, expected=1638.010254\n",
      "predicted=1639.989445, expected=1640.000000\n",
      "predicted=1642.276314, expected=1622.650391\n",
      "predicted=1625.001625, expected=1607.949829\n",
      "predicted=1610.229810, expected=1627.579590\n",
      "predicted=1629.699694, expected=1622.100342\n",
      "predicted=1624.364591, expected=1619.440186\n",
      "predicted=1621.675883, expected=1631.560425\n",
      "predicted=1633.727764, expected=1633.000366\n",
      "predicted=1635.239751, expected=1636.399658\n",
      "predicted=1638.632718, expected=1641.089722\n",
      "predicted=1643.323928, expected=1639.830322\n",
      "predicted=1642.097816, expected=1671.729736\n",
      "predicted=1673.852933, expected=1696.170166\n",
      "predicted=1698.435936, expected=1692.430420\n",
      "predicted=1694.847032, expected=1668.950073\n",
      "predicted=1671.408989, expected=1625.949829\n",
      "predicted=1628.302613, expected=1620.799927\n",
      "predicted=1622.995444, expected=1670.619751\n",
      "predicted=1672.749199, expected=1673.099854\n",
      "predicted=1675.409228, expected=1690.809814\n",
      "predicted=1693.110168, expected=1686.219727\n",
      "predicted=1688.586482, expected=1712.359741\n",
      "predicted=1714.671755, expected=1742.150146\n",
      "predicted=1744.556643, expected=1761.849976\n",
      "predicted=1764.352638, expected=1797.270264\n",
      "predicted=1799.863213, expected=1819.259888\n",
      "predicted=1821.949164, expected=1764.770142\n",
      "predicted=1767.425732, expected=1774.260010\n",
      "predicted=1776.804540, expected=1783.760010\n",
      "predicted=1786.327905, expected=1765.700195\n",
      "predicted=1768.289513, expected=1773.420044\n",
      "predicted=1775.954691, expected=1780.749878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1783.302736, expected=1814.189575\n",
      "predicted=1816.762543, expected=1813.980225\n",
      "predicted=1816.641340, expected=1820.699707\n",
      "predicted=1823.356639, expected=1818.859985\n",
      "predicted=1821.532770, expected=1837.279541\n",
      "predicted=1839.940548, expected=1849.860107\n",
      "predicted=1852.572666, expected=1835.839844\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1838.586558, expected=1847.330200\n",
      "predicted=1850.027714, expected=1844.070435\n",
      "predicted=1846.802553, expected=1843.060303\n",
      "predicted=1845.778646, expected=1844.869873\n",
      "predicted=1847.580454, expected=1863.040161\n",
      "predicted=1865.744648, expected=1864.819458\n",
      "predicted=1867.578992, expected=1861.690308\n",
      "predicted=1864.453547, expected=1887.309814\n",
      "predicted=1890.042685, expected=1923.770264\n",
      "predicted=1926.599743, expected=1901.750366\n",
      "predicted=1904.683062, expected=1902.250000\n",
      "predicted=1905.101753, expected=1950.630005\n",
      "0.9177767991455493\n",
      "\n",
      "\n",
      "Printing Mean Squared Error of Predictions...\n",
      "Test MSE: 0.000519\n"
     ]
    }
   ],
   "source": [
    "size = int(len(ts_log)*(0.7))\n",
    "train, test = ts_log[0:size], ts_log[size:len(ts_log)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "\n",
    "print('Printing Predicted vs Expected Values...')\n",
    "print('\\n')\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(0,1,1)) #The order(p,d,q) of the model\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()[0]\n",
    "    yhat = output[0]\n",
    "    predictions.append(float(yhat))\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    print('predicted=%f, expected=%f' % (np.exp(yhat), np.exp(obs)))\n",
    "\n",
    "    \n",
    "error = mean_squared_error(test, predictions)\n",
    "r2 = r2_score(test, predictions)\n",
    "print(r2)\n",
    "\n",
    "print('\\n')\n",
    "print('Printing Mean Squared Error of Predictions...')\n",
    "print('Test MSE: %.6f' % error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_series = pd.Series(predictions, index = test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a plot to check actual vs predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(title='Prediction (ARIMA)', xlabel='Date(weekly)', ylabel='Price($)')\n",
    "ax.plot((np.exp(test)), label='observed', color='r')\n",
    "ax.plot(np.exp(predictions_series), color='g', label='rolling one-step out-of-sample forecast')\n",
    "legend = ax.legend(loc='upper left')\n",
    "legend.get_frame().set_facecolor('w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9177767991455493"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above model was run with values of parameters (p,d,q) set randomly as (0,1,1). The model performs with an accuracy of 91.7%. We need to tune the parameters to get the best combination of (p,d,q) to see if the model performs better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning hyper parameters for ARIMA model\n",
    "\n",
    "Evaluating combinations of p, d and q values for ARIMA model to find the best combination of p, d, q values for our model with different range of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 1) RMSE=3.680\n",
      "ARIMA(0, 1, 1) RMSE=0.022\n",
      "ARIMA(0, 1, 2) RMSE=0.022\n",
      "ARIMA(0, 1, 3) RMSE=0.022\n",
      "ARIMA(0, 1, 4) RMSE=0.022\n",
      "ARIMA(0, 2, 1) RMSE=0.021\n",
      "ARIMA(0, 2, 2) RMSE=0.022\n",
      "ARIMA(1, 0, 0) RMSE=0.021\n",
      "ARIMA(1, 1, 0) RMSE=0.022\n",
      "ARIMA(1, 2, 0) RMSE=0.027\n",
      "ARIMA(2, 1, 0) RMSE=0.022\n",
      "ARIMA(2, 2, 0) RMSE=0.025\n",
      "ARIMA(2, 2, 1) RMSE=0.022\n",
      "ARIMA(3, 1, 0) RMSE=0.022\n",
      "ARIMA(3, 1, 1) RMSE=0.022\n",
      "ARIMA(3, 2, 0) RMSE=0.025\n",
      "ARIMA(3, 2, 1) RMSE=0.022\n",
      "ARIMA(3, 2, 2) RMSE=0.022\n",
      "ARIMA(4, 1, 0) RMSE=0.022\n",
      "ARIMA(4, 2, 0) RMSE=0.024\n",
      "ARIMA(4, 2, 1) RMSE=0.022\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from pandas import Series\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "order=(2,1,1)\n",
    "# evaluate an ARIMA model for a given order (p,d,q) and return RMSE\n",
    "def evaluate_arima_model(X, arima_order):\n",
    "    # prepare training dataset\n",
    "    X = X.astype('float32')\n",
    "    train_size = int(len(X) * 0.50)\n",
    "    train, test = X[0:train_size], X[train_size:]\n",
    "    history = [x for x in train]\n",
    "    # make predictions\n",
    "    predictions = list()\n",
    "    for t in range(len(test)):\n",
    "        model = ARIMA(history, order=arima_order)\n",
    "        # model_fit = model.fit(disp=0)\n",
    "        model_fit = model.fit(trend='nc', disp=0)\n",
    "        yhat = model_fit.forecast()[0]\n",
    "        predictions.append(yhat)\n",
    "        history.append(test[t])\n",
    "    # calculate out of sample error\n",
    "    mse = mean_squared_error(test, predictions)\n",
    "    rmse = sqrt(mse)\n",
    "    return rmse\n",
    "\n",
    "# evaluate combinations of p, d and q values for an ARIMA model\n",
    "def evaluate_models(dataset, p_values, d_values, q_values):\n",
    "    dataset = dataset.astype('float32')\n",
    "    best_score, best_cfg = float(\"inf\"), None\n",
    "    for p in p_values:\n",
    "        for d in d_values:\n",
    "            for q in q_values:\n",
    "                order = (p,d,q)\n",
    "                try:\n",
    "                    mse = evaluate_arima_model(dataset, (order))\n",
    "                    if mse < best_score:\n",
    "                        best_score, best_cfg = mse, order\n",
    "                    print('ARIMA%s RMSE=%.3f' % (order,mse))\n",
    "                except:\n",
    "                    continue\n",
    "    #print('Best ARIMA%s RMSE=%.3f' % (best_cfg, best_score))\n",
    "\n",
    "# evaluate parameters\n",
    "p_values = range(0, 5)\n",
    "d_values = range(0, 3)\n",
    "q_values = range(0, 5)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "evaluate_models(ts_log, p_values, d_values, q_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The combination of p,d,q values for which the RMSE is lowest is chosen as the best combination. In our case, there are a couple of combinations with least RMSE of 0.021 and we will be comparing the accuracy of the corresponding model, to find the best one.\n",
    "\n",
    "#### 1. Order = (0,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Predicted vs Expected Values...\n",
      "\n",
      "\n",
      "predicted=1633.336377, expected=1641.539673\n",
      "predicted=1645.333383, expected=1665.270264\n",
      "predicted=1669.252781, expected=1696.349731\n",
      "predicted=1700.603221, expected=1695.749634\n",
      "predicted=1700.028719, expected=1689.300415\n",
      "predicted=1693.498357, expected=1683.989990\n",
      "predicted=1688.105032, expected=1689.119995\n",
      "predicted=1693.238700, expected=1698.749756\n",
      "predicted=1702.930060, expected=1704.860229\n",
      "predicted=1709.078464, expected=1723.859619\n",
      "predicted=1728.217637, expected=1715.970093\n",
      "predicted=1720.269062, expected=1723.789795\n",
      "predicted=1728.110735, expected=1734.779907\n",
      "predicted=1739.174460, expected=1750.079956\n",
      "predicted=1754.592624, expected=1730.219727\n",
      "predicted=1734.568444, expected=1715.669922\n",
      "predicted=1719.838449, expected=1663.149902\n",
      "predicted=1666.850780, expected=1691.089600\n",
      "predicted=1694.888378, expected=1660.510376\n",
      "predicted=1664.103229, expected=1701.450073\n",
      "predicted=1705.275968, expected=1699.799927\n",
      "predicted=1703.661339, expected=1713.780273\n",
      "predicted=1717.720872, expected=1693.960327\n",
      "predicted=1697.746877, expected=1699.730225\n",
      "predicted=1703.501529, expected=1710.629639\n",
      "predicted=1714.479954, expected=1739.020386\n",
      "predicted=1743.075610, expected=1743.070068\n",
      "predicted=1747.179224, expected=1754.999634\n",
      "predicted=1759.184360, expected=1796.619873\n",
      "predicted=1801.125514, expected=1813.029907\n",
      "predicted=1817.712627, expected=1822.489868\n",
      "predicted=1827.248081, expected=1843.929688\n",
      "predicted=1848.844028, expected=1842.919678\n",
      "predicted=1847.837185, expected=1812.970215\n",
      "predicted=1817.615739, expected=1813.699951\n",
      "predicted=1818.269871, expected=1802.000244\n",
      "predicted=1806.448083, expected=1829.239868\n",
      "predicted=1833.860329, expected=1863.609741\n",
      "predicted=1868.518237, expected=1808.000122\n",
      "predicted=1812.499195, expected=1817.270142\n",
      "predicted=1821.719330, expected=1779.219604\n",
      "predicted=1783.361506, expected=1777.439697\n",
      "predicted=1781.477007, expected=1797.170044\n",
      "predicted=1801.327562, expected=1834.329590\n",
      "predicted=1838.780782, expected=1823.290405\n",
      "predicted=1827.692606, expected=1847.749634\n",
      "predicted=1852.294374, expected=1862.479736\n",
      "predicted=1867.153032, expected=1886.519775\n",
      "predicted=1891.375787, expected=1898.520264\n",
      "predicted=1903.482909, expected=1886.300293\n",
      "predicted=1891.157564, expected=1896.199585\n",
      "predicted=1901.083665, expected=1919.650391\n",
      "predicted=1924.706870, expected=1882.619751\n",
      "predicted=1887.394674, expected=1886.519775\n",
      "predicted=1891.236572, expected=1882.220337\n",
      "predicted=1886.879090, expected=1876.709595\n",
      "predicted=1881.290963, expected=1883.419800\n",
      "predicted=1888.015649, expected=1904.899658\n",
      "predicted=1909.642365, expected=1902.899658\n",
      "predicted=1907.634582, expected=1905.390137\n",
      "predicted=1910.114180, expected=1927.680176\n",
      "predicted=1932.548732, expected=1932.819824\n",
      "predicted=1937.735004, expected=1998.100464\n",
      "predicted=2003.501660, expected=2002.380127\n",
      "predicted=2007.892922, expected=2012.709961\n",
      "predicted=2018.278470, expected=2039.509888\n",
      "predicted=2045.275494, expected=1994.819824\n",
      "predicted=2000.244318, expected=1958.310425\n",
      "predicted=1963.350424, expected=1952.069458\n",
      "predicted=1956.974759, expected=1939.010254\n",
      "predicted=1943.773974, expected=1987.149902\n",
      "predicted=1992.229098, expected=1990.000244\n",
      "predicted=1995.151893, expected=1989.870239\n",
      "predicted=1994.996514, expected=1970.189575\n",
      "predicted=1975.133704, expected=1908.030396\n",
      "predicted=1912.449990, expected=1941.050049\n",
      "predicted=1945.587772, expected=1926.420410\n",
      "predicted=1930.876728, expected=1944.299683\n",
      "predicted=1948.838492, expected=1915.009644\n",
      "predicted=1919.332776, expected=1934.359619\n",
      "predicted=1938.750999, expected=1974.549683\n",
      "predicted=1979.240950, expected=1974.850098\n",
      "predicted=1979.581379, expected=2012.979736\n",
      "predicted=2017.965336, expected=2002.999878\n",
      "predicted=2007.942616, expected=2004.360352\n",
      "predicted=2009.269795, expected=1971.309692\n",
      "predicted=1975.949046, expected=1952.760254\n",
      "predicted=1957.185648, expected=1909.420288\n",
      "predicted=1913.484930, expected=1889.650146\n",
      "predicted=1893.482350, expected=1864.420288\n",
      "predicted=1868.023989, expected=1870.320312\n",
      "predicted=1873.901796, expected=1755.249878\n",
      "predicted=1758.055004, expected=1719.360229\n",
      "predicted=1721.746068, expected=1788.609741\n",
      "predicted=1791.358492, expected=1760.949585\n",
      "predicted=1763.610809, expected=1819.960083\n",
      "predicted=1822.938586, expected=1831.730103\n",
      "predicted=1834.860852, expected=1770.720337\n",
      "predicted=1773.457218, expected=1764.029785\n",
      "predicted=1766.612066, expected=1789.299805\n",
      "predicted=1792.011219, expected=1768.700073\n",
      "predicted=1771.304374, expected=1664.200195\n",
      "predicted=1666.111382, expected=1782.170166\n",
      "predicted=1784.621357, expected=1642.809814\n",
      "predicted=1644.585314, expected=1538.879761\n",
      "predicted=1539.858859, expected=1530.420410\n",
      "predicted=1531.187224, expected=1598.010132\n",
      "predicted=1599.119929, expected=1665.530029\n",
      "predicted=1667.112050, expected=1665.530029\n",
      "predicted=1667.204497, expected=1627.800049\n",
      "predicted=1629.249232, expected=1642.809814\n",
      "predicted=1644.270663, expected=1755.490112\n",
      "predicted=1757.612400, expected=1754.910034\n",
      "predicted=1757.194874, expected=1712.430054\n",
      "predicted=1714.444509, expected=1636.849976\n",
      "predicted=1638.347721, expected=1631.169922\n",
      "predicted=1632.514397, expected=1599.010132\n",
      "predicted=1600.158296, expected=1619.440186\n",
      "predicted=1620.638924, expected=1593.409668\n",
      "predicted=1594.489862, expected=1512.289673\n",
      "predicted=1512.896161, expected=1495.460205\n",
      "predicted=1495.857090, expected=1516.729614\n",
      "predicted=1517.197242, expected=1502.060059\n",
      "predicted=1502.479329, expected=1581.330200\n",
      "predicted=1582.119877, expected=1581.419922\n",
      "predicted=1582.324517, expected=1677.750244\n",
      "predicted=1679.160555, expected=1673.569824\n",
      "predicted=1675.090487, expected=1690.169800\n",
      "predicted=1691.762916, expected=1772.359985\n",
      "predicted=1774.429630, expected=1668.400269\n",
      "predicted=1669.985614, expected=1699.189697\n",
      "predicted=1700.786389, expected=1629.130249\n",
      "predicted=1630.381849, expected=1641.030273\n",
      "predicted=1642.230003, expected=1643.239990\n",
      "predicted=1644.456708, expected=1663.540161\n",
      "predicted=1664.857402, expected=1658.380249\n",
      "predicted=1659.687532, expected=1591.909790\n",
      "predicted=1592.851274, expected=1520.909912\n",
      "predicted=1521.392671, expected=1551.479614\n",
      "predicted=1552.000131, expected=1495.080200\n",
      "predicted=1495.365728, expected=1460.829712\n",
      "predicted=1460.872626, expected=1377.449951\n",
      "predicted=1377.070686, expected=1343.960083\n",
      "predicted=1340.288931, expected=1470.899902\n",
      "predicted=1470.741471, expected=1461.640015\n",
      "predicted=1461.629719, expected=1478.019897\n",
      "predicted=1478.065936, expected=1501.969849\n",
      "predicted=1502.142313, expected=1539.130005\n",
      "predicted=1539.503711, expected=1500.279907\n",
      "predicted=1500.518887, expected=1575.389771\n",
      "predicted=1575.921599, expected=1629.510132\n",
      "predicted=1630.409645, expected=1656.579834\n",
      "predicted=1657.687503, expected=1659.420410\n",
      "predicted=1660.571060, expected=1656.219604\n",
      "predicted=1657.346597, expected=1640.560059\n",
      "predicted=1641.594469, expected=1617.210083\n",
      "predicted=1618.093657, expected=1674.560425\n",
      "predicted=1675.688220, expected=1683.780396\n",
      "predicted=1685.031682, expected=1693.219849\n",
      "predicted=1694.520105, expected=1696.200073\n",
      "predicted=1697.518123, expected=1632.169678\n",
      "predicted=1633.158929, expected=1640.020264\n",
      "predicted=1640.946468, expected=1654.929688\n",
      "predicted=1655.935781, expected=1670.569580\n",
      "predicted=1671.661339, expected=1637.889893\n",
      "predicted=1638.832199, expected=1593.880127\n",
      "predicted=1594.552665, expected=1670.430176\n",
      "predicted=1671.405947, expected=1718.729858\n",
      "predicted=1720.048725, expected=1626.229736\n",
      "predicted=1627.141010, expected=1633.310303\n",
      "predicted=1634.120230, expected=1658.810425\n",
      "predicted=1659.746024, expected=1640.259644\n",
      "predicted=1641.130493, expected=1614.370117\n",
      "predicted=1615.083481, expected=1588.219604\n",
      "predicted=1588.764903, expected=1591.000000\n",
      "predicted=1591.515863, expected=1638.010254\n",
      "predicted=1638.758205, expected=1640.000000\n",
      "predicted=1640.797632, expected=1622.650391\n",
      "predicted=1623.362290, expected=1607.949829\n",
      "predicted=1608.558187, expected=1627.579590\n",
      "predicted=1628.251887, expected=1622.100342\n",
      "predicted=1622.765514, expected=1619.440186\n",
      "predicted=1620.078215, expected=1631.560425\n",
      "predicted=1632.243230, expected=1633.000366\n",
      "predicted=1633.697551, expected=1636.399658\n",
      "predicted=1637.106561, expected=1641.089722\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1641.816664, expected=1639.830322\n",
      "predicted=1640.547352, expected=1671.729736\n",
      "predicted=1672.589148, expected=1696.170166\n",
      "predicted=1697.179632, expected=1692.430420\n",
      "predicted=1693.448492, expected=1668.950073\n",
      "predicted=1669.840495, expected=1625.949829\n",
      "predicted=1626.599656, expected=1620.799927\n",
      "predicted=1621.357880, expected=1670.619751\n",
      "predicted=1671.394744, expected=1673.099854\n",
      "predicted=1673.946416, expected=1690.809814\n",
      "predicted=1691.732364, expected=1686.219727\n",
      "predicted=1687.138463, expected=1712.359741\n",
      "predicted=1713.385008, expected=1742.150146\n",
      "predicted=1743.348675, expected=1761.849976\n",
      "predicted=1763.171909, expected=1797.270264\n",
      "predicted=1798.783060, expected=1819.259888\n",
      "predicted=1820.920439, expected=1764.770142\n",
      "predicted=1766.176878, expected=1774.260010\n",
      "predicted=1775.631336, expected=1783.760010\n",
      "predicted=1785.176874, expected=1765.700195\n",
      "predicted=1767.030354, expected=1773.420044\n",
      "predicted=1774.754068, expected=1780.749878\n",
      "predicted=1782.122516, expected=1814.189575\n",
      "predicted=1815.721698, expected=1813.980225\n",
      "predicted=1815.547214, expected=1820.699707\n",
      "predicted=1822.288918, expected=1818.859985\n",
      "predicted=1820.434995, expected=1837.279541\n",
      "predicted=1838.931822, expected=1849.860107\n",
      "predicted=1851.588169, expected=1835.839844\n",
      "predicted=1837.501805, expected=1847.330200\n",
      "predicted=1849.023558, expected=1844.070435\n",
      "predicted=1845.746364, expected=1843.060303\n",
      "predicted=1844.716004, expected=1844.869873\n",
      "predicted=1846.520553, expected=1863.040161\n",
      "predicted=1864.771766, expected=1864.819458\n",
      "predicted=1866.572355, expected=1861.690308\n",
      "predicted=1863.417131, expected=1887.309814\n",
      "predicted=1889.147738, expected=1923.770264\n",
      "predicted=1925.816273, expected=1901.750366\n",
      "predicted=1903.717687, expected=1902.250000\n",
      "predicted=1904.178940, expected=1950.630005\n",
      "0.9177671592302777\n",
      "\n",
      "\n",
      "Printing Mean Squared Error of Predictions...\n",
      "Test MSE: 0.000519\n"
     ]
    }
   ],
   "source": [
    "size = int(len(ts_log)*(0.7))\n",
    "train, test = ts_log[0:size], ts_log[size:len(ts_log)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "\n",
    "print('Printing Predicted vs Expected Values...')\n",
    "print('\\n')\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(0,2,1)) #The order(p,d,q) of the model\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()[0]\n",
    "    yhat = output[0]\n",
    "    predictions.append(float(yhat))\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    print('predicted=%f, expected=%f' % (np.exp(yhat), np.exp(obs)))\n",
    "\n",
    "    \n",
    "error = mean_squared_error(test, predictions)\n",
    "r2 = r2_score(test, predictions)\n",
    "print(r2)\n",
    "\n",
    "print('\\n')\n",
    "print('Printing Mean Squared Error of Predictions...')\n",
    "print('Test MSE: %.6f' % error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_series = pd.Series(predictions, index = test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploting graph and calculating accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(title='Prediction (ARIMA)', xlabel='Date(weekly)', ylabel='$(Dollar)')\n",
    "ax.plot((np.exp(test)), label='observed', color='r')\n",
    "ax.plot(np.exp(predictions_series), color='g', label='rolling one-step out-of-sample forecast')\n",
    "legend = ax.legend(loc='upper left')\n",
    "legend.get_frame().set_facecolor('w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9177671592302777"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy for Order = (0,2,1) is 91.7%\n",
    "\n",
    "#### 2. Order = (1,0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_series = pd.Series(predictions, index = test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(title='Prediction (ARIMA)', xlabel='Date(weekly)', ylabel='$(Dollar)')\n",
    "ax.plot((np.exp(test)), label='observed', color='r')\n",
    "ax.plot(np.exp(predictions_series), color='g', label='rolling one-step out-of-sample forecast')\n",
    "legend = ax.legend(loc='upper left')\n",
    "legend.get_frame().set_facecolor('w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9177671592302777"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy for Order = (1,0,0) = 91.8%\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations Results\n",
    "\n",
    "* The above graph represents the comparison of our observed stock close prices vs forecasted close price for the upcoming week with an accuracy of 91.8%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prettytable import PrettyTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------+----------+\n",
      "| Model(ARIMA(p,d,q)) | Accuracy |\n",
      "+---------------------+----------+\n",
      "|     ARIMA(0,1,1)    |   91.7   |\n",
      "|     ARIMA(0,2,1)    |   91.7   |\n",
      "|     ARIMA(1,0,0)    |   91.8   |\n",
      "+---------------------+----------+\n"
     ]
    }
   ],
   "source": [
    "result_hyperparam = PrettyTable()\n",
    "\n",
    "result_hyperparam.field_names = [\"Model(ARIMA(p,d,q))\", \"Accuracy\"]\n",
    "\n",
    "result_hyperparam.add_row(['ARIMA(0,1,1)',91.7])\n",
    "result_hyperparam.add_row(['ARIMA(0,2,1)',91.7])\n",
    "result_hyperparam.add_row(['ARIMA(1,0,0)',91.80])\n",
    "\n",
    "\n",
    "print(result_hyperparam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----------------+--------------+\n",
      "|    Date    | ARIMA_Predicted | ARIMA_Actual |\n",
      "+------------+-----------------+--------------+\n",
      "| 2019-04-22 |     1861.05     |   1887.31    |\n",
      "| 2019-04-23 |     1886.67     |   1923.77    |\n",
      "| 2019-04-24 |     1923.12     |   1901.75    |\n",
      "| 2019-04-25 |     1901.11     |   1902.25    |\n",
      "| 2019-04-26 |     1901.61     |   1946.19    |\n",
      "+------------+-----------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "result_predictedvsexpected = PrettyTable()\n",
    "\n",
    "result_predictedvsexpected.field_names = [\"Date\", \"ARIMA_Predicted\",\"ARIMA_Actual\"]\n",
    "\n",
    "result_predictedvsexpected.add_row(['2019-04-22',1861.05,1887.31])\n",
    "result_predictedvsexpected.add_row(['2019-04-23',1886.67,1923.77])\n",
    "result_predictedvsexpected.add_row(['2019-04-24',1923.12,1901.75])\n",
    "result_predictedvsexpected.add_row(['2019-04-25',1901.11,1902.25])\n",
    "result_predictedvsexpected.add_row(['2019-04-26',1901.61,1946.19])\n",
    "\n",
    "print(result_predictedvsexpected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part B\n",
    "\n",
    "### LSTM\n",
    "\n",
    "Long Short Term Memory(LSTM) are the most common types of Recurrent Neural Networks used for sequential data. LSTMs have an edge over conventional feed-forward neural networks and RNN in many ways. This is because of their property of selectively remembering patterns for long durations of time.\n",
    "LSTMs make small modifications to the information by multiplications and additions. With LSTMs, the information flows through a mechanism known as cell states. This way, LSTMs can selectively remember or forget things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-04-26</th>\n",
       "      <td>616.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-27</th>\n",
       "      <td>606.570007</td>\n",
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       "    <tr>\n",
       "      <th>2016-04-28</th>\n",
       "      <td>602.000000</td>\n",
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       "      <th>2016-04-29</th>\n",
       "      <td>659.590027</td>\n",
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       "      <th>2016-05-02</th>\n",
       "      <td>683.849976</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-03</th>\n",
       "      <td>671.320007</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-04</th>\n",
       "      <td>670.900024</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-05</th>\n",
       "      <td>659.090027</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-06</th>\n",
       "      <td>673.950012</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>679.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-10</th>\n",
       "      <td>703.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-11</th>\n",
       "      <td>713.229980</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-12</th>\n",
       "      <td>717.929993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-13</th>\n",
       "      <td>709.919983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-16</th>\n",
       "      <td>710.659973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-17</th>\n",
       "      <td>695.270020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-18</th>\n",
       "      <td>697.450012</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-19</th>\n",
       "      <td>698.520020</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-20</th>\n",
       "      <td>702.799988</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-23</th>\n",
       "      <td>696.750000</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-24</th>\n",
       "      <td>704.200012</td>\n",
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       "    <tr>\n",
       "      <th>2016-05-25</th>\n",
       "      <td>708.349976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-26</th>\n",
       "      <td>714.909973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-27</th>\n",
       "      <td>712.239990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>722.789978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>719.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-02</th>\n",
       "      <td>728.239990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-03</th>\n",
       "      <td>725.539978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-06</th>\n",
       "      <td>726.729980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>723.739990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>1712.359985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>1742.150024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>1761.849976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>1797.270020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>1819.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>1764.770020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>1774.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>1783.760010</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>1765.699951</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>1773.420044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>1780.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>1814.189941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-02</th>\n",
       "      <td>1813.979980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-03</th>\n",
       "      <td>1820.699951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-04</th>\n",
       "      <td>1818.859985</td>\n",
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       "    <tr>\n",
       "      <th>2019-04-05</th>\n",
       "      <td>1837.280029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-08</th>\n",
       "      <td>1849.859985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09</th>\n",
       "      <td>1835.839966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-10</th>\n",
       "      <td>1847.329956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-11</th>\n",
       "      <td>1844.069946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-12</th>\n",
       "      <td>1843.060059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-15</th>\n",
       "      <td>1844.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-16</th>\n",
       "      <td>1863.040039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-17</th>\n",
       "      <td>1864.819946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-18</th>\n",
       "      <td>1861.689941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-22</th>\n",
       "      <td>1887.310059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-23</th>\n",
       "      <td>1923.770020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-24</th>\n",
       "      <td>1901.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-25</th>\n",
       "      <td>1902.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-26</th>\n",
       "      <td>1950.630005</td>\n",
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       "<p>756 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close\n",
       "Date                   \n",
       "2016-04-26   616.880005\n",
       "2016-04-27   606.570007\n",
       "2016-04-28   602.000000\n",
       "2016-04-29   659.590027\n",
       "2016-05-02   683.849976\n",
       "2016-05-03   671.320007\n",
       "2016-05-04   670.900024\n",
       "2016-05-05   659.090027\n",
       "2016-05-06   673.950012\n",
       "2016-05-09   679.750000\n",
       "2016-05-10   703.070007\n",
       "2016-05-11   713.229980\n",
       "2016-05-12   717.929993\n",
       "2016-05-13   709.919983\n",
       "2016-05-16   710.659973\n",
       "2016-05-17   695.270020\n",
       "2016-05-18   697.450012\n",
       "2016-05-19   698.520020\n",
       "2016-05-20   702.799988\n",
       "2016-05-23   696.750000\n",
       "2016-05-24   704.200012\n",
       "2016-05-25   708.349976\n",
       "2016-05-26   714.909973\n",
       "2016-05-27   712.239990\n",
       "2016-05-31   722.789978\n",
       "2016-06-01   719.440002\n",
       "2016-06-02   728.239990\n",
       "2016-06-03   725.539978\n",
       "2016-06-06   726.729980\n",
       "2016-06-07   723.739990\n",
       "...                 ...\n",
       "2019-03-15  1712.359985\n",
       "2019-03-18  1742.150024\n",
       "2019-03-19  1761.849976\n",
       "2019-03-20  1797.270020\n",
       "2019-03-21  1819.260010\n",
       "2019-03-22  1764.770020\n",
       "2019-03-25  1774.260010\n",
       "2019-03-26  1783.760010\n",
       "2019-03-27  1765.699951\n",
       "2019-03-28  1773.420044\n",
       "2019-03-29  1780.750000\n",
       "2019-04-01  1814.189941\n",
       "2019-04-02  1813.979980\n",
       "2019-04-03  1820.699951\n",
       "2019-04-04  1818.859985\n",
       "2019-04-05  1837.280029\n",
       "2019-04-08  1849.859985\n",
       "2019-04-09  1835.839966\n",
       "2019-04-10  1847.329956\n",
       "2019-04-11  1844.069946\n",
       "2019-04-12  1843.060059\n",
       "2019-04-15  1844.869995\n",
       "2019-04-16  1863.040039\n",
       "2019-04-17  1864.819946\n",
       "2019-04-18  1861.689941\n",
       "2019-04-22  1887.310059\n",
       "2019-04-23  1923.770020\n",
       "2019-04-24  1901.750000\n",
       "2019-04-25  1902.250000\n",
       "2019-04-26  1950.630005\n",
       "\n",
       "[756 rows x 1 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['Date','ds','y'])\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scl = MinMaxScaler(feature_range = (0, 1))\n",
    "train = scl.fit_transform(df)\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a 7 day window to run the model on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "523\n",
      "225\n",
      "523\n",
      "225\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def processData(data,lb):\n",
    "    X,Y = [],[]\n",
    "    for i in range(len(data)-lb-1):\n",
    "        X.append(data[i:(i+lb),0])\n",
    "        Y.append(data[(i+lb),0])\n",
    "    return np.array(X),np.array(Y)\n",
    "X,y = processData(train,7)\n",
    "X_train,X_test = X[:int(X.shape[0]*0.70)],X[int(X.shape[0]*0.70):]\n",
    "y_train,y_test = y[:int(y.shape[0]*0.70)],y[int(y.shape[0]*0.70):]\n",
    "print(X_train.shape[0])\n",
    "print(X_test.shape[0])\n",
    "print(y_train.shape[0])\n",
    "print(y_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping data & Building Lstm \n",
    "* Also checking loss and loss_Val for different epoch values and plotting the graph for the same\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\Dt\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 523 samples, validate on 225 samples\n",
      "Epoch 1/10\n",
      "523/523 [==============================] - 2s 3ms/step - loss: 0.0021 - val_loss: 0.0034\n",
      "Epoch 2/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0122 - val_loss: 0.0094\n",
      "Epoch 3/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0144 - val_loss: 0.0144\n",
      "Epoch 4/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0035 - val_loss: 0.0045\n",
      "Epoch 5/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0063 - val_loss: 0.0020\n",
      "Epoch 6/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0032 - val_loss: 0.0071\n",
      "Epoch 7/10\n",
      "523/523 [==============================] - ETA: 0s - loss: 0.003 - 1s 1ms/step - loss: 0.0035 - val_loss: 0.0022\n",
      "Epoch 8/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0026 - val_loss: 0.0029\n",
      "Epoch 9/10\n",
      "523/523 [==============================] - 1s 2ms/step - loss: 0.0049 - val_loss: 0.0045\n",
      "Epoch 10/10\n",
      "523/523 [==============================] - 1s 1ms/step - loss: 0.0063 - val_loss: 0.0020\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(256,input_shape=(7,1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam',loss='mse')\n",
    "#Reshape data for (Sample,Timestep,Features) \n",
    "X_train = X_train.reshape((X_train.shape[0],X_train.shape[1],1))\n",
    "X_test = X_test.reshape((X_test.shape[0],X_test.shape[1],1))\n",
    "#Fit model with history to check for overfitting\n",
    "history = model.fit(X_train,y_train,epochs=10,validation_data=(X_test,y_test),shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x265132f9f60>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x265151c9160>]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xt = model.predict(X_test)\n",
    "plt.plot(scl.inverse_transform(y_test.reshape(-1,1)))\n",
    "plt.plot(scl.inverse_transform(Xt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "act = []\n",
    "pred = []\n",
    "i=224\n",
    "Xt = model.predict(X_test[i].reshape(1,7,1))\n",
    "pred.append(scl.inverse_transform(Xt))\n",
    "act.append(scl.inverse_transform(y_test[i].reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = pd.DataFrame({'pred':list(np.reshape(pred, (-1))),'act':list(np.reshape(act, (-1)))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x265152229e8>]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xt = model.predict(X_test)\n",
    "plt.plot(scl.inverse_transform(y_test.reshape(-1,1)))\n",
    "plt.plot(scl.inverse_transform(Xt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual = scl.inverse_transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = scl.inverse_transform(Xt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [str(i) for i in range(0, len(actual))]\n",
    "act = pd.DataFrame(actual, index=index)\n",
    "act.columns = ['Actual']\n",
    "\n",
    "index = [str(i) for i in range(0, len(pred))]\n",
    "pre = pd.DataFrame(pred, index=index)\n",
    "pre.columns = ['Predicted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1865.433838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>1870.625488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>1877.360840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1890.429199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>1904.372681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Predicted\n",
       "220  1865.433838\n",
       "221  1870.625488\n",
       "222  1877.360840\n",
       "223  1890.429199\n",
       "224  1904.372681"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining the metrics for measure using sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7829044005114965"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.metrics import r2_score\n",
    "sklearn.metrics.r2_score(act, pre, sample_weight=None, multioutput='uniform_average')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+----------------+-------------+\n",
      "|    Date    | LSTM_Predicted | LSTM_Actual |\n",
      "+------------+----------------+-------------+\n",
      "| 2019-04-22 |    1865.43     |   1887.31   |\n",
      "| 2019-04-23 |    1870.62     |   1923.77   |\n",
      "| 2019-04-24 |    1877.36     |   1901.75   |\n",
      "| 2019-04-25 |    1890.42     |   1902.25   |\n",
      "| 2019-04-26 |    1904.37     |   1946.19   |\n",
      "+------------+----------------+-------------+\n"
     ]
    }
   ],
   "source": [
    "lstm_predictedvsexpected = PrettyTable()\n",
    "\n",
    "lstm_predictedvsexpected.field_names = [\"Date\", \"LSTM_Predicted\",\"LSTM_Actual\"]\n",
    "\n",
    "lstm_predictedvsexpected.add_row(['2019-04-22',1865.43,1887.31])\n",
    "lstm_predictedvsexpected.add_row(['2019-04-23',1870.62,1923.77])\n",
    "lstm_predictedvsexpected.add_row(['2019-04-24',1877.36,1901.75])\n",
    "lstm_predictedvsexpected.add_row(['2019-04-25',1890.42,1902.25])\n",
    "lstm_predictedvsexpected.add_row(['2019-04-26',1904.37,1946.19])\n",
    "\n",
    "print(lstm_predictedvsexpected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Evaluation Table: LSTM | FbProphet | ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------+----------------+---------------------+-----------------+\n",
      "|    Date    |  Actual | LSTM_Predicted | FbProphet_Predicted | Arima_Predicted |\n",
      "+------------+---------+----------------+---------------------+-----------------+\n",
      "| 2019-04-22 | 1887.31 |    1865.43     |       1893.47       |     1861.05     |\n",
      "| 2019-04-23 | 1923.77 |    1870.62     |       1898.39       |     1886.67     |\n",
      "| 2019-04-24 | 1901.75 |    1877.36     |       1895.39       |     1923.12     |\n",
      "| 2019-04-25 | 1902.25 |    1890.42     |       1889.51       |     1901.11     |\n",
      "| 2019-04-26 | 1946.19 |    1904.37     |       1917.81       |     1901.61     |\n",
      "+------------+---------+----------------+---------------------+-----------------+\n"
     ]
    }
   ],
   "source": [
    "evaluation_table = PrettyTable()\n",
    "\n",
    "evaluation_table.field_names = [\"Date\",\"Actual\",\"LSTM_Predicted\",\"FbProphet_Predicted\",\"Arima_Predicted\"]\n",
    "\n",
    "evaluation_table.add_row(['2019-04-22',1887.31,1865.43,1893.47,1861.05])\n",
    "evaluation_table.add_row(['2019-04-23',1923.77,1870.62,1898.39,1886.67])\n",
    "evaluation_table.add_row(['2019-04-24',1901.75,1877.36,1895.39,1923.12])\n",
    "evaluation_table.add_row(['2019-04-25',1902.25,1890.42,1889.51,1901.11])\n",
    "evaluation_table.add_row(['2019-04-26',1946.19,1904.37,1917.81,1901.61])\n",
    "\n",
    "print(evaluation_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation by Accuracy metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+--------------------+----------------+\n",
      "| LSTM_Accuracy | FbProphet_Accuracy | Arima_Accuracy |\n",
      "+---------------+--------------------+----------------+\n",
      "|     78.29     |        96.5        |      91.8      |\n",
      "+---------------+--------------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "Accuracy_table = PrettyTable()\n",
    "\n",
    "Accuracy_table.field_names = [\"LSTM_Accuracy\",\"FbProphet_Accuracy\",\"Arima_Accuracy\"]\n",
    "Accuracy_table.add_row([78.29,96.5,91.8])\n",
    "print(Accuracy_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "\n",
    "* It can be seen from the evaluation table how the models have performed with respect to Actual value and also the accuracy scores of the models\n",
    "* It can be concluded that Fbprophet performed better in comparision with the other models, even after Tunning\n",
    "* We have tried running our code on cloud platform like Databricks and Google Cloud dataprocs, we were able to successful run on databricks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contribution: \n",
    "* Tuning of hyperparameters of ARIMA model\n",
    "* Spark implementation for data loading and eda\n",
    "* Implementing the code on Databricks\n",
    "* Implementing the code on Google cloud Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References:\n",
    "* https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\n",
    "* https://databricks.com/spark/getting-started-with-apache-spark\n",
    "* https://github.com/rahulworld/bitcoin-prediction/blob/master/Bitcoin_ARIMA.ipynb\n",
    "* https://heartbeat.fritz.ai/a-beginners-guide-to-implementing-long-short-term-memory-networks-lstm-eb7a2ff09a27\n",
    "* https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.adfuller.html\n",
    "* https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit\n",
    "* https://spark.apache.org/docs/2.3.1/api/python/index.html\n",
    "* https://cloud.google.com/dataproc/\n",
    "* https://cloud.google.com/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licensing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2019 Vinitha Ravichandran | Ranga Chari.V\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE\n",
    "THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE (\"CCPL\" OR \"LICENSE\"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.\n",
    "\n",
    "BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS."
   ]
  }
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